TY - JOUR AU - Lester, Corey AU - Rowell, Brigid AU - Zheng, Yifan AU - Co, Zoe AU - Marshall, Vincent AU - Kim, Yong Jin AU - Chen, Qiyuan AU - Kontar, Raed AU - Yang, Jessie X. PY - 2025/4/18 TI - Effect of Uncertainty-Aware AI Models on Pharmacists? Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial JO - JMIR Med Inform SP - e64902 VL - 13 KW - artificial intelligence KW - AI KW - human-computer interaction KW - decision-making KW - human factors KW - randomized controlled trial KW - clinical decision support KW - prediction KW - pharmacist KW - verification KW - drug development KW - drug KW - diagnosis KW - clinical decision support systems N2 - Background: Artificial intelligence (AI)?based clinical decision support systems are increasingly used in health care. Uncertainty-aware AI presents the model?s confidence in its decision alongside its prediction, whereas black-box AI only provides a prediction. Little is known about how this type of AI affects health care providers? work performance and reaction time. Objective: This study aimed to determine the effects of black-box and uncertainty-aware AI advice on pharmacist decision-making and reaction time. Methods: Recruitment emails were sent to pharmacists through professional listservs describing a web-based, crossover, randomized controlled trial. Participants were randomized to the black-box AI or uncertainty-aware AI condition in a 1:1 manner. Participants completed 100 mock verification tasks with AI help and 100 without AI help. The order of no help and AI help was randomized. Participants were exposed to correct and incorrect prescription fills, where the correct decision was to ?accept? or ?reject,? respectively. AI help provided correct (79%) or incorrect (21%) advice. Reaction times, participant decisions, AI advice, and AI help type were recorded for each verification. Likelihood ratio tests compared means across the three categories of AI type for each level of AI correctness. Results: A total of 30 participants provided complete datasets. An equal number of participants were in each AI condition. Participants? decision-making performance and reaction times differed across the 3 conditions. Accurate AI recommendations resulted in the rejection of the incorrect drug 96.1% and 91.8% of the time for uncertainty-aware AI and black-box AI respectively, compared with 81.2% without AI help. Correctly dispensed medications were accepted at rates of 99.2% with black-box help, 94.1% with uncertainty-aware AI help, and 94.6% without AI help. Uncertainty-aware AI protected against bad AI advice to approve an incorrectly filled medication compared with black-box AI (83.3% vs 76.7%). When the AI recommended rejecting a correctly filled medication, pharmacists without AI help had a higher rate of correctly accepting the medication (94.6%) compared with uncertainty-aware AI help (86.2%) and black-box AI help (81.2%). Uncertainty-aware AI resulted in shorter reaction times than black-box AI and no AI help except in the scenario where ?AI rejects the correct drug.? Black-box AI did not lead to reduced reaction times compared with pharmacists acting alone. Conclusions: Pharmacists? performance and reaction times varied by AI type and AI accuracy. Overall, uncertainty-aware AI resulted in faster decision-making and acted as a safeguard against bad AI advice to approve a misfilled medication. Conversely, black-box AI had the longest reaction times, and user performance degraded in the presence of bad AI advice. However, uncertainty-aware AI could result in unnecessary double-checks, but it is preferred over false negative advice, where patients receive the wrong medication. These results highlight the importance of well-designed AI that addresses users? needs, enhances performance, and avoids overreliance on AI. Trial Registration: ClinicalTrials.gov NCT06795477; https://clinicaltrials.gov/study/NCT06795477 UR - https://medinform.jmir.org/2025/1/e64902 UR - http://dx.doi.org/10.2196/64902 ID - info:doi/10.2196/64902 ER - TY - JOUR AU - Trinkley, E. Katy AU - Simon, T. Steven AU - Rosenberg, A. Michael PY - 2025/4/14 TI - Impact of an Alert-Based Inpatient Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome: Large-Scale, System-Wide Observational Study JO - J Med Internet Res SP - e68256 VL - 27 KW - drug-induced QT prolongation KW - predictive modeling KW - electronic health records KW - clinical decision support KW - alert-based CDS system KW - tools KW - long QT syndrome KW - prevention N2 - Background: Prevention of drug-induced QT prolongation (diLQTS) has been the focus of many system-wide clinical decision support (CDS) tools, which can be directly embedded within the framework of the electronic health record system and triggered to alert in high-risk patients when a known QT-prolonging medication is ordered. Justification for these CDS systems typically lies in the ability to accurately predict which patients are at high risk; however, it is not always evident that identification of risk alone is sufficient for appropriate CDS implementation. Objective: In this investigation, we examined the impact of a system-wide, alert-based, inpatient CDS tool designed to prevent diLQTS across 10 known QT-prolonging medications. Methods: We compared the risk of diLQTS, duration of hospitalization, and in- and out-of-hospital mortality before and after implementation of the CDS system in 178,097 hospitalizations among 102,847 patients. We also compared outcomes between those in whom an alert fired and those in whom it did not, and within the various responses to the alert by providers. Analyses were adjusted for age, sex, race and ethnicity, inpatient location, electrolyte values, and comorbidities, with the latter processed using an unsupervised clustering analysis applied to the top 500 most common medications and diagnosis codes, respectively. Results: We found that the simple, rule-based logic of the CDS (any prior electrocardiograph with heart rate?corrected QT interval (QTc)?500 ms) successfully identified patients at high risk of diLQTS with an odds ratio of 2.28 (95% CI 2.10-2.47, P<.001) among those in whom it fired. However, we did not identify any impact on the risk of diLQTS based on provider responses or on the risk of inpatient, 3-month, 6-month, or 1-year mortality. When compared with rates prior to implementation, the risk of diLQTS was not significantly different after the CDS tools were deployed across the system, although mortality was significantly higher after the tools were implemented. Conclusions: We found that despite successful identification of high-risk patients for diLQTS, deployment of an alert-based CDS did not impact the risk of diLQTS. These findings suggest that quantification of high risk may be insufficient rationale for implementation of a CDS system and that hospital systems should consider evaluation of the system in its entirety prior to adoption to improve clinical outcomes. UR - https://www.jmir.org/2025/1/e68256 UR - http://dx.doi.org/10.2196/68256 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68256 ER - TY - JOUR AU - Socrates, Vimig AU - Wright, S. Donald AU - Huang, Thomas AU - Fereydooni, Soraya AU - Dien, Christine AU - Chi, Ling AU - Albano, Jesse AU - Patterson, Brian AU - Sasidhar Kanaparthy, Naga AU - Wright, X. Catherine AU - Loza, Andrew AU - Chartash, David AU - Iscoe, Mark AU - Taylor, Andrew Richard PY - 2025/4/11 TI - Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study JO - JMIR Aging SP - e69504 VL - 8 KW - deprescribing KW - large language models KW - geriatrics KW - potentially inappropriate medication list KW - emergency medicine KW - natural language processing KW - calibration N2 - Background: Polypharmacy, the concurrent use of multiple medications, is prevalent among older adults and associated with increased risks for adverse drug events including falls. Deprescribing, the systematic process of discontinuing potentially inappropriate medications, aims to mitigate these risks. However, the practical application of deprescribing criteria in emergency settings remains limited due to time constraints and criteria complexity. Objective: This study aims to evaluate the performance of a large language model (LLM)?based pipeline in identifying deprescribing opportunities for older emergency department (ED) patients with polypharmacy, using 3 different sets of criteria: Beers, Screening Tool of Older People?s Prescriptions, and Geriatric Emergency Medication Safety Recommendations. The study further evaluates LLM confidence calibration and its ability to improve recommendation performance. Methods: We conducted a retrospective cohort study of older adults presenting to an ED in a large academic medical center in the Northeast United States from January 2022 to March 2022. A random sample of 100 patients (712 total oral medications) was selected for detailed analysis. The LLM pipeline consisted of two steps: (1) filtering high-yield deprescribing criteria based on patients? medication lists, and (2) applying these criteria using both structured and unstructured patient data to recommend deprescribing. Model performance was assessed by comparing model recommendations to those of trained medical students, with discrepancies adjudicated by board-certified ED physicians. Selective prediction, a method that allows a model to abstain from low-confidence predictions to improve overall reliability, was applied to assess the model?s confidence and decision-making thresholds. Results: The LLM was significantly more effective in identifying deprescribing criteria (positive predictive value: 0.83; negative predictive value: 0.93; McNemar test for paired proportions: ?21=5.985; P=.02) relative to medical students, but showed limitations in making specific deprescribing recommendations (positive predictive value=0.47; negative predictive value=0.93). Adjudication revealed that while the model excelled at identifying when there was a deprescribing criterion related to one of the patient?s medications, it often struggled with determining whether that criterion applied to the specific case due to complex inclusion and exclusion criteria (54.5% of errors) and ambiguous clinical contexts (eg, missing information; 39.3% of errors). Selective prediction only marginally improved LLM performance due to poorly calibrated confidence estimates. Conclusions: This study highlights the potential of LLMs to support deprescribing decisions in the ED by effectively filtering relevant criteria. However, challenges remain in applying these criteria to complex clinical scenarios, as the LLM demonstrated poor performance on more intricate decision-making tasks, with its reported confidence often failing to align with its actual success in these cases. The findings underscore the need for clearer deprescribing guidelines, improved LLM calibration for real-world use, and better integration of human?artificial intelligence workflows to balance artificial intelligence recommendations with clinician judgment. UR - https://aging.jmir.org/2025/1/e69504 UR - http://dx.doi.org/10.2196/69504 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/69504 ER - TY - JOUR AU - Chan, Fan-Ying AU - Ku, Yi-En AU - Lie, Wen-Nung AU - Chen, Hsiang-Yin PY - 2025/4/10 TI - Web-Based Explainable Machine Learning-Based Drug Surveillance for Predicting Sunitinib- and Sorafenib-Associated Thyroid Dysfunction: Model Development and Validation Study JO - JMIR Form Res SP - e67767 VL - 9 KW - thyroid dysfunction KW - machine learning KW - cancer KW - sunitinib KW - sorafenib KW - TKI KW - tyrosine kinase inhibitor N2 - Background: Unlike one-snap data collection methods that only identify high-risk patients, machine learning models using time-series data can predict adverse events and aid in the timely management of cancer. Objective: This study aimed to develop and validate machine learning models for sunitinib- and sorafenib-associated thyroid dysfunction using a time-series data collection approach. Methods: Time series data of patients first prescribed sunitinib or sorafenib were collected from a deidentified clinical research database. Logistic regression, random forest, adaptive Boosting, Light Gradient-Boosting Machine, and Gradient Boosting Decision Tree were used to develop the models. Prediction performances were compared using the accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and area under the precision-recall curve. The optimal threshold for the best-performing model was selected based on the maximum F1-score. SHapley Additive exPlanations analysis was conducted to assess feature importance and contributions at both the cohort and patient levels. Results: The training cohort included 609 patients, while the temporal validation cohort had 198 patients. The Gradient Boosting Decision Tree model without resampling outperformed other models, with area under the precision-recall curve of 0.600, area under the receiver operating characteristic curve of 0.876, and F1-score of 0.583 after adjusting the threshold. The SHapley Additive exPlanations analysis identified higher cholesterol levels, longer summed days of medication use, and clear cell adenocarcinoma histology as the most important features. The final model was further integrated into a web-based application. Conclusions: This model can serve as an explainable adverse drug reaction surveillance system for predicting sunitinib- and sorafenib-associated thyroid dysfunction. UR - https://formative.jmir.org/2025/1/e67767 UR - http://dx.doi.org/10.2196/67767 ID - info:doi/10.2196/67767 ER - TY - JOUR AU - Ji, Huanhuan AU - Gong, Meiling AU - Gong, Li AU - Zhang, Ni AU - Zhou, Ruiou AU - Deng, Dongmei AU - Yang, Ya AU - Song, Lin AU - Jia, Yuntao PY - 2025/3/25 TI - Detection of Clinically Significant Drug-Drug Interactions in Fatal Torsades de Pointes: Disproportionality Analysis of the Food and Drug Administration Adverse Event Reporting System JO - J Med Internet Res SP - e65872 VL - 27 KW - torsades de pointes KW - FAERS database KW - drug-drug interactions KW - QTc-prolonging drugs KW - adverse drug events N2 - Background: Torsades de pointes (TdP) is a rare yet potentially fatal cardiac arrhythmia that is often drug-induced. Drug-drug interactions (DDIs) are a major risk factor for TdP development, but the specific drug combinations that increase this risk have not been extensively studied. Objective: This study aims to identify clinically significant, high-priority DDIs to provide a foundation to minimize the risk of TdP and effectively manage DDI risks in the future. Methods: We used the following 4 frequency statistical models to detect DDI signals using the Food and Drug Administration Adverse Event Reporting System (FAERS) database: ? shrinkage measure, combination risk ratio, chi-square statistic, and additive model. The adverse event of interest was TdP, and the drugs targeted were all registered and classified as ?suspect,? ?interacting,? or ?concomitant drugs? in FAERS. The DDI signals were identified and evaluated using the Lexicomp and Drugs.com databases, supplemented with real-world data from the literature. Results: As of September 2023, this study included 4313 TdP cases, with 721 drugs and 4230 drug combinations that were reported for at least 3 cases. The ? shrinkage measure model demonstrated the most conservative signal detection, whereas the chi-square statistic model exhibited the closest similarity in signal detection tendency to the ? shrinkage measure model. The ? value was 0.972 (95% CI 0.942-1.002), and the Ppositive and Pnegative values were 0.987 and 0.985, respectively. We detected 2158 combinations using the 4 frequency statistical models, of which 241 combinations were indexed by Drugs.com or Lexicomp and 105 were indexed by both. The most commonly interacting drugs were amiodarone, citalopram, quetiapine, ondansetron, ciprofloxacin, methadone, escitalopram, sotalol, and voriconazole. The most common combinations were citalopram and quetiapine, amiodarone and ciprofloxacin, amiodarone and escitalopram, amiodarone and fluoxetine, ciprofloxacin and sotalol, and amiodarone and citalopram. Although 38 DDIs were indexed by Drugs.com and Lexicomp, they were not detected by any of the 4 models. Conclusions: Clinical evidence on DDIs is limited, and not all combinations of heart rate?corrected QT interval (QTc)?prolonging drugs result in TdP, even when involving high-risk drugs or those with known risk of TdP. This study provides a comprehensive real-world overview of drug-induced TdP, delimiting both clinically significant DDIs and negative DDIs, providing valuable insights into the safety profiles of various drugs, and informing the optimization of clinical practice. UR - https://www.jmir.org/2025/1/e65872 UR - http://dx.doi.org/10.2196/65872 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65872 ER - TY - JOUR AU - Amoozegar, B. Jacqueline AU - Williams, Peyton AU - Giombi, C. Kristen AU - Richardson, Courtney AU - Shenkar, Ella AU - Watkins, L. Rebecca AU - O'Donoghue, C. Amie AU - Sullivan, W. Helen PY - 2025/3/25 TI - Consumer Engagement With Risk Information on Prescription Drug Social Media Pages: Findings From In-Depth Interviews JO - J Med Internet Res SP - e67361 VL - 27 KW - social media KW - prescription drugs KW - risk information KW - safety information KW - Facebook KW - Instagram KW - prescription KW - risk KW - information KW - safety KW - interview KW - consumer engagement KW - digital KW - drug promotion KW - user experience KW - promotion N2 - Background: The volume of digital drug promotion has grown over time, and social media has become a source of information about prescription drugs for many consumers. Pharmaceutical companies currently present risk information about prescription drugs they promote in a variety of ways within and across social media platforms. There is scarce research on consumers? interactions with prescription drug promotion on social media, particularly on which features may facilitate or inhibit consumers? ability to find, review, and comprehend drug information. This is concerning because it is critical for consumers to know and weigh drug benefits and risks to be able to make informed decisions regarding medical treatment. Objective: We aimed to develop an understanding of the user interface (UI) and user experience (UX) of social media pages and posts created by pharmaceutical companies to promote drugs and how UI or UX design features impact consumers? interactions with drug information. Methods: We conducted in-person interviews with 54 consumers segmented into groups by device type (laptop or mobile phone), social media platform (Facebook or Instagram), and age. Interviewers asked participants to navigate to and review a series of 4 pages and 3 posts on their assigned device and platform. Interviewers encouraged participants to ?think aloud,? as they interacted with the stimuli during a brief observation period. Following each observation period, participants were asked probing questions. An analyst reviewed video recordings of the observation periods to abstract quantitative interaction data on whether a participant clicked on or viewed risk information at each location it appeared on each page. Participants? responses were organized in a metamatrix, which we used to conduct thematic analysis. Results: Observational data revealed that 59% of participants using Facebook and 70% of participants using Instagram viewed risk information in at least 1 possible location on average across all pages tested during the observation period. There was not a single location across the Facebook pages that participants commonly clicked on to view risk information. However, a video with scrolling risk information attracted more views than other features. On Instagram, at least half of the participants consistently clicked on the highlighted story with risk information across the pages. Although thematic analysis showed that most participants were able to identify the official pages and risk information for each drug, auto-scrolling text and text size posed barriers to identification and comprehensive review for some participants. Participants generally found it more difficult to identify the drugs? indications than risks. Participants using Instagram more frequently reported challenges identifying risks and indications compared to those using Facebook. Conclusions: UI or UX design features can facilitate or pose barriers to users? identification, review, and comprehension of the risk information provided on prescription drugs? social media pages and posts. UR - https://www.jmir.org/2025/1/e67361 UR - http://dx.doi.org/10.2196/67361 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67361 ER - TY - JOUR AU - Hooten, Michael W. AU - Erickson, J. Darin AU - Chawarski, Marek AU - Scholz, A. Natalie AU - Waljee, F. Jennifer AU - Brummett, M. Chad AU - Jeffery, M. Molly PY - 2025/3/24 TI - Unintended Prolonged Opioid Use: Protocol for a Case-Controlled Trial JO - JMIR Res Protoc SP - e72032 VL - 14 KW - opioid use KW - case-control KW - unintended opioid use KW - prolonged opioid use KW - prospective N2 - Background: Misuse of prescription opioids remains a public health problem. Appropriate short-term use of these medications in opioid-naive patients is indicated in selected settings but can result in unintended prolonged opioid use (UPOU), defined as the continuation of opioid therapy beyond the period by which acute pain would have been expected to resolve. Clinical strategies aimed at preventing UPOU are lacking due to the absence of information about how this poorly understood clinical phenomenon actually develops. Objective: In this research project, 3 Clinical and Translational Science Awards (CTSA) programs (Mayo Clinic, University of Michigan, and Yale University) leveraged the conceptual framework for UPOU to investigate how patient characteristics, practice environment characteristics, and opioid prescriber characteristics facilitate or impede UPOU. All data management and analyses were conducted at a fourth CTSA program (University of Minnesota). This work was accomplished by pursuing 3 specific aims. Methods: In aim 1, opioid-naive adults receiving an initial opioid prescription were recruited for study participation. Opioid prescriptions were identified longitudinally, and patterns of use were categorized as short-term, episodic, or long-term use using established criteria. Using a prospective case-control design, patients progressing to UPOU were matched 1:1 with patients who did not develop UPOU, and differences in patient characteristics were assessed. In aim 2, clinicians who prescribed opioids to patients in aim 1 were identified and recruited for prospective assessments. Institutional and individual practice environments were assessed using a validated self-report survey. In aim 3, structural equation modeling was used to evaluate data collected in aims 1 and 2, and identified interactions were further evaluated in a large national administrative claims database. Results: Patient recruitment began on August 1, 2019. However, due to the COVID-19 pandemic, patient recruitment was slowed and intermittently interrupted over the ensuing 3-year period. As a result of regional variations in the impact of the COVID-19 pandemic on research activities, the majority of patient and clinician recruitment occurred at the Mayo Clinic site. Conclusions: Following complete data analyses, it is anticipated that electronic health record systems will be leveraged to help clinicians identify at risk patients and to develop direct-to-patient educational materials to raise awareness of the risk factors for developing UPOU. Trial Registration: ClinicalTrials.gov NCT04024397; https://clinicaltrials.gov/study/NCT04024397 International Registered Report Identifier (IRRID): DERR1-10.2196/72032 UR - https://www.researchprotocols.org/2025/1/e72032 UR - http://dx.doi.org/10.2196/72032 UR - http://www.ncbi.nlm.nih.gov/pubmed/39992690 ID - info:doi/10.2196/72032 ER - TY - JOUR AU - Wang, Mengmeng AU - Wang, Lianxin AU - Liu, Fumei AU - Chen, Renbo AU - Wang, Zhifei AU - Cui, Xin AU - Li, Yuanyuan AU - Xie, Yanming PY - 2025/3/21 TI - Clinical Safety of Pudilan Xiaoyan Oral Liquid for the Treatment of Upper Respiratory Tract Infection in the Real World: Protocol for a Prospective, Observational, Registry Study JO - JMIR Res Protoc SP - e65789 VL - 14 KW - Pudilan Xiaoyan oral liquid KW - PDL KW - upper respiratory tract infection KW - URTI KW - registry KW - adverse drug reaction N2 - Background: Pudilan Xiaoyan oral liquid (PDL) is a proprietary Chinese medicine preparation widely used for upper respiratory tract infection, known for its significant therapeutic effects. However, the safety profiles reported in several observational studies vary, and these studies primarily focus on efficacy rather than specifically addressing safety concerns, thus representing inadequate safety monitoring. Objective: This study aimed to investigate the incidence of adverse drug reactions (ADRs) associated with PDL and explore the factors contributing to these reactions. Methods: The study is a prospective, observational, multicenter, hospital-based surveillance study. A total of 17 hospitals from China are involved. The study is expected to enroll a large sample of 10,000 patients aged between 18 and 80 years with upper respiratory tract infection who were prescribed PDL. The patients? data, including demographics, medical history, diagnostic information, medication details, adverse events, and laboratory test results, will be monitored. The occurrence of ADRs will be recorded. The primary outcome is the incidence of ADR. Secondary outcomes are the ratio of patients whose body temperature return to the normal range (cases of body temperature normalization and the duration for achieving normal body temperature within a 3-day period will be documented) and changes in liver and kidney function (occurrence of drug-induced liver injury and acute kidney injury). Descriptive analyses will be performed for the primary and secondary outcomes. A cohort, nested, case-control study design will be used. If one patient has an ADR, then 4 patients without ADRs will be matched as the control group according to gender, age within 5 years, drug batch, and other factors, at a ratio of 1?4 to compare the symptoms related to ADRs. The differences of ADR incidence among the possible influencing factors will be compared separately to find the factors with large differences. Then, synthetic minority oversampling technique and group least absolute shrinkage and selection operator methods will be used to identify factors influencing the occurrence of ADRs. Finally, propensity scoring methods will be used to control for confounding variables. The progress of each subcenter will be closely monitored, and the incidence of ADR will be systematically calculated. Furthermore, the characteristics and influencing factors of ADR will be analyzed, along with an investigation into its geographical distribution. Results: The study began on July 17, 2019. Due to the limited number of eligible patients, missed follow-ups, and the huge clinical burden caused by public health events in 2019, the final case will be enrolled on August 30, 2025. Conclusions: This study will obtain safety results of PDL in the real world and provide guidance on the clinical safety of traditional Chinese medicine formulations. Trial Registration: ClinicalTrials.gov NCT04031651; https://clinicaltrials.gov/study/NCT04031651 International Registered Report Identifier (IRRID): DERR1-10.2196/65789 UR - https://www.researchprotocols.org/2025/1/e65789 UR - http://dx.doi.org/10.2196/65789 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65789 ER - TY - JOUR AU - Haegens, L. Lex AU - Huiskes, B. Victor J. AU - van den Bemt, F. Bart J. AU - Bekker, L. Charlotte PY - 2025/3/13 TI - Factors Influencing the Intentions of Patients With Inflammatory Rheumatic Diseases to Use a Digital Human for Medication Information: Qualitative Study JO - J Med Internet Res SP - e57697 VL - 27 KW - digital human KW - information provision KW - intention to use KW - qualitative study KW - focus groups KW - drug-related problems KW - medication safety KW - safety information KW - information seeking KW - Netherlands KW - Pharmacotherapy KW - medication KW - telehealth KW - communication technologies KW - medication information KW - rheumatic diseases KW - rheumatology N2 - Background: Introduction: Patients with inflammatory rheumatic diseases (IRDs) frequently experience drug-related problems (DRPs). DRPs can have negative health consequences and should be addressed promptly to prevent complications. A digital human, which is an embodied conversational agent, could provide medication-related information in a time- and place-independent manner to support patients in preventing and decreasing DRPs. Objective: This study aims to identify factors that influence the intention of patients with IRDs to use a digital human to retrieve medication-related information. Methods: A qualitative study with 3 in-person focus groups was conducted among adult patients diagnosed with an IRD in the Netherlands. The prototype of a digital human is an innovative tool that provides spoken answers to medication-related questions and provides information linked to the topic, such as (instructional) videos, drug leaflets, and other relevant sources. Before the focus group, participants completed a preparatory exercise at home to become familiar with the digital human. A semistructured interview guide based on the Proctor framework for implementation determinants was used to interview participants about the acceptability, adoption, appropriateness, costs, feasibility, fidelity, penetration, and sustainability of the digital human. Focus groups were recorded, transcribed, and analyzed thematically. Results: The participants included 22 patients, with a median age of 68 (IQR 52-75) years, of whom 64% (n=22) were female. In total, 6 themes describing factors influencing patients? intention to use a digital human were identified: (1) the degree to which individual needs for medication-related information are met; (2) confidence in one?s ability to use the digital human; (3) the degree to which using the digital human resembles interacting with a human; (4) technical functioning of the digital human; (5) privacy and security; and (6) expected benefit of using the digital human. Conclusions: The intention of patients with IRDs to use a novel digital human to retrieve medication-related information was influenced by factors related to each patient?s information needs and confidence in their ability to use the digital human, features of the digital human, and the expected benefits of using the digital human. These identified themes should be considered during the further development of the digital human and during implementation to increase intention to use and future adoption. Thereafter, the effect of applying a digital human as an instrument to improve patients? self-management regarding DRPs could be researched. UR - https://www.jmir.org/2025/1/e57697 UR - http://dx.doi.org/10.2196/57697 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57697 ER - TY - JOUR AU - Lau, Jerry AU - Bisht, Shivani AU - Horton, Robert AU - Crisan, Annamaria AU - Jones, John AU - Gantotti, Sandeep AU - Hermes-DeSantis, Evelyn PY - 2025/3/13 TI - Creation of Scientific Response Documents for Addressing Product Medical Information Inquiries: Mixed Method Approach Using Artificial Intelligence JO - JMIR AI SP - e55277 VL - 4 KW - AI KW - LLM KW - GPT KW - biopharmaceutical KW - medical information KW - content generation KW - artificial intelligence KW - pharmaceutical KW - scientific response KW - documentation KW - information KW - clinical data KW - strategy KW - reference KW - feasibility KW - development KW - machine learning KW - large language model KW - accuracy KW - context KW - traceability KW - accountability KW - survey KW - scientific response documentation KW - SRD KW - benefit KW - content generator KW - content analysis KW - Generative Pre-trained Transformer N2 - Background: Pharmaceutical manufacturers address health care professionals? information needs through scientific response documents (SRDs), offering evidence-based answers to medication and disease state questions. Medical information departments, staffed by medical experts, develop SRDs that provide concise summaries consisting of relevant background information, search strategies, clinical data, and balanced references. With an escalating demand for SRDs and the increasing complexity of therapies, medical information departments are exploring advanced technologies and artificial intelligence (AI) tools like large language models (LLMs) to streamline content development. While AI and LLMs show promise in generating draft responses, a synergistic approach combining an LLM with traditional machine learning classifiers in a series of human-supervised and -curated steps could help address limitations, including hallucinations. This will ensure accuracy, context, traceability, and accountability in the development of the concise clinical data summaries of an SRD. Objective: This study aims to quantify the challenges of SRD development and develop a framework exploring the feasibility and value addition of integrating AI capabilities in the process of creating concise summaries for an SRD. Methods: To measure the challenges in SRD development, a survey was conducted by phactMI, a nonprofit consortium of medical information leaders in the pharmaceutical industry, assessing aspects of SRD creation among its member companies. The survey collected data on the time and tediousness of various activities related to SRD development. Another working group, consisting of medical information professionals and data scientists, used AI to aid SRD authoring, focusing on data extraction and abstraction. They used logistic regression on semantic embedding features to train classification models and transformer-based summarization pipelines to generate concise summaries. Results: Of the 33 companies surveyed, 64% (21/33) opened the survey, and 76% (16/21) of those responded. On average, medical information departments generate 614 new documents and update 1352 documents each year. Respondents considered paraphrasing scientific articles to be the most tedious and time-intensive task. In the project?s second phase, sentence classification models showed the ability to accurately distinguish target categories with receiver operating characteristic scores ranging from 0.67 to 0.85 (all P<.001), allowing for accurate data extraction. For data abstraction, the comparison of the bilingual evaluation understudy (BLEU) score and semantic similarity in the paraphrased texts yielded different results among reviewers, with each preferring different trade-offs between these metrics. Conclusions: This study establishes a framework for integrating LLM and machine learning into SRD development, supported by a pharmaceutical company survey emphasizing the challenges of paraphrasing content. While machine learning models show potential for section identification and content usability assessment in data extraction and abstraction, further optimization and research are essential before full-scale industry implementation. The working group?s insights guide an AI-driven content analysis; address limitations; and advance efficient, precise, and responsive frameworks to assist with pharmaceutical SRD development. UR - https://ai.jmir.org/2025/1/e55277 UR - http://dx.doi.org/10.2196/55277 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55277 ER - TY - JOUR AU - Yanagisawa, Yuki AU - Watabe, Satoshi AU - Yokoyama, Sakura AU - Sayama, Kyoko AU - Kizaki, Hayato AU - Tsuchiya, Masami AU - Imai, Shungo AU - Someya, Mitsuhiro AU - Taniguchi, Ryoo AU - Yada, Shuntaro AU - Aramaki, Eiji AU - Hori, Satoko PY - 2025/3/11 TI - Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition JO - JMIR Cancer SP - e69663 VL - 11 KW - natural language processing KW - pharmaceutical care records KW - androgen receptor axis-targeting agents KW - adverse events KW - outpatient care N2 - Background: Androgen receptor axis-targeting reagents (ARATs) have become key drugs for patients with castration-resistant prostate cancer (CRPC). ARATs are taken long term in outpatient settings, and effective adverse event (AE) monitoring can help prolong treatment duration for patients with CRPC. Despite the importance of monitoring, few studies have identified which AEs can be captured and assessed in community pharmacies, where pharmacists in Japan dispense medications, provide counseling, and monitor potential AEs for outpatients prescribed ARATs. Therefore, we anticipated that a named entity recognition (NER) system might be used to extract AEs recorded in pharmaceutical care records generated by community pharmacists. Objective: This study aimed to evaluate whether an NER system can effectively and systematically identify AEs in outpatients undergoing ARAT therapy by reviewing pharmaceutical care records generated by community pharmacists, focusing on assessment notes, which often contain detailed records of AEs. Additionally, the study sought to determine whether outpatient pharmacotherapy monitoring can be enhanced by using NER to systematically collect AEs from pharmaceutical care records. Methods: We used an NER system based on the widely used Japanese medical term extraction system MedNER-CR-JA, which uses Bidirectional Encoder Representations from Transformers (BERT). To evaluate its performance for pharmaceutical care records by community pharmacists, the NER system was first applied to 1008 assessment notes in records related to anticancer drug prescriptions. Three pharmaceutically proficient researchers compared the results with the annotated notes assigned symptom tags according to annotation guidelines and evaluated the performance of the NER system on the assessment notes in the pharmaceutical care records. The system was then applied to 2193 assessment notes for patients prescribed ARATs. Results: The F1-score for exact matches of all symptom tags between the NER system and annotators was 0.72, confirming the NER system has sufficient performance for application to pharmaceutical care records. The NER system automatically assigned 1900 symptom tags for the 2193 assessment notes from patients prescribed ARATs; 623 tags (32.8%) were positive symptom tags (symptoms present), while 1067 tags (56.2%) were negative symptom tags (symptoms absent). Positive symptom tags included ARAT-related AEs such as ?pain,? ?skin disorders,? ?fatigue,? and ?gastrointestinal symptoms.? Many other symptoms were classified as serious AEs. Furthermore, differences in symptom tag profiles reflecting pharmacists? AE monitoring were observed between androgen synthesis inhibition and androgen receptor signaling inhibition. Conclusions: The NER system successfully extracted AEs from pharmaceutical care records of patients prescribed ARATs, demonstrating its potential to systematically track the presence and absence of AEs in outpatients. Based on the analysis of a large volume of pharmaceutical medical records using the NER system, community pharmacists not only detect potential AEs but also actively monitor the absence of severe AEs, offering valuable insights for the continuous improvement of patient safety management. UR - https://cancer.jmir.org/2025/1/e69663 UR - http://dx.doi.org/10.2196/69663 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/69663 ER - TY - JOUR AU - Park, Adam AU - Jung, Young Se AU - Yune, Ilha AU - Lee, Ho-Young PY - 2025/3/7 TI - Applying Robotic Process Automation to Monitor Business Processes in Hospital Information Systems: Mixed Method Approach JO - JMIR Med Inform SP - e59801 VL - 13 KW - robotic process automation KW - RPA KW - electronic medical records KW - EMR KW - system monitoring KW - health care information systems KW - user-centric monitoring KW - performance evaluation KW - business process management KW - BPM KW - healthcare technology KW - mixed methods research KW - process automation in health care N2 - Background: Electronic medical records (EMRs) have undergone significant changes due to advancements in technology, including artificial intelligence, the Internet of Things, and cloud services. The increasing complexity within health care systems necessitates enhanced process reengineering and system monitoring approaches. Robotic process automation (RPA) provides a user-centric approach to monitoring system complexity by mimicking end user interactions, thus presenting potential improvements in system performance and monitoring. Objective: This study aimed to explore the application of RPA in monitoring the complexities of EMR systems within a hospital environment, focusing on RPA?s ability to perform end-to-end performance monitoring that closely reflects real-time user experiences. Methods: The research was conducted at Seoul National University Bundang Hospital using a mixed methods approach. It included the iterative development and integration of RPA bots programmed to simulate and monitor typical user interactions with the hospital?s EMR system. Quantitative data from RPA process outputs and qualitative insights from interviews with system engineers and managers were used to evaluate the effectiveness of RPA in system monitoring. Results: RPA bots effectively identified and reported system inefficiencies and failures, providing a bridge between end user experiences and engineering assessments. The bots were particularly useful in detecting delays and errors immediately following system updates or interactions with external services. Over 3 years, RPA monitoring highlighted discrepancies between user-reported experiences and traditional engineering metrics, with the bots frequently identifying critical system issues that were not evident from standard component-level monitoring. Conclusions: RPA enhances system monitoring by providing insights that reflect true end user experiences, which are often overlooked by traditional monitoring methods. The study confirms the potential of RPA to act as a comprehensive monitoring tool within complex health care systems, suggesting that RPA can significantly contribute to the maintenance and improvement of EMR systems by providing a more accurate and timely reflection of system performance and user satisfaction. UR - https://medinform.jmir.org/2025/1/e59801 UR - http://dx.doi.org/10.2196/59801 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053771 ID - info:doi/10.2196/59801 ER - TY - JOUR AU - Fekete, Tibor János AU - Gy?rffy, Balázs PY - 2025/3/6 TI - MetaAnalysisOnline.com: Web-Based Tool for the Rapid Meta-Analysis of Clinical and Epidemiological Studies JO - J Med Internet Res SP - e64016 VL - 27 KW - statistics KW - pharmacology KW - treatment KW - epidemiology KW - fixed effect model KW - random effect model KW - hazard rate KW - response rate KW - clinical trial KW - funnel plot KW - z score plot N2 - Background: A meta-analysis is a quantitative, formal study design in epidemiology and clinical medicine that systematically integrates and quantitatively synthesizes findings from multiple independent studies. This approach not only enhances statistical power but also enables the exploration of effects across diverse populations and helps resolve controversies arising from conflicting studies. Objective: This study aims to develop and implement a user-friendly tool for conducting meta-analyses, addressing the need for an accessible platform that simplifies the complex statistical procedures required for evidence synthesis while maintaining methodological rigor. Methods: The platform available at MetaAnalysisOnline.com enables comprehensive meta-analyses through an intuitive web interface, requiring no programming expertise or command-line operations. The system accommodates diverse data types including binary (total and event numbers), continuous (mean and SD), and time-to-event data (hazard rates with CIs), while implementing both fixed-effect and random-effect models using established statistical approaches such as DerSimonian-Laird, Mantel-Haenszel, and inverse variance methods for effect size estimation and heterogeneity assessment. Results: In addition to statistical tests, graphical representations including the forest plot, the funnel plot, and the z score plot can be drawn. A forest plot is highly effective in illustrating heterogeneity and pooled results. The risk of publication bias can be revealed by a funnel plot. A z score plot provides a visual assessment of whether more research is needed to establish a reliable conclusion. All the discussed models and visualization options are integrated into the registration-free web-based portal. Leveraging MetaAnalysisOnline.com's capabilities, we examined treatment-related adverse events in patients with cancer receiving perioperative anti?PD-1 immunotherapy through a systematic review encompassing 10 studies with 8099 total participants. Meta-analysis revealed that anti?PD-1 therapy doubled the risk of adverse events (risk ratio 2.15, 95% CI 1.39-3.32), with significant between-study heterogeneity (I2=95%) and publication bias detected through the Egger test (P=.02). While these findings suggest increased toxicity associated with anti?PD-1 treatment, the z score analysis indicated that additional studies are needed for definitive conclusions. Conclusions: In summary, the web-based tool aims to bridge the void for clinical and life science researchers by offering a user-friendly alternative for the swift and reproducible meta-analysis of clinical and epidemiological trials. UR - https://www.jmir.org/2025/1/e64016 UR - http://dx.doi.org/10.2196/64016 UR - http://www.ncbi.nlm.nih.gov/pubmed/39928123 ID - info:doi/10.2196/64016 ER - TY - JOUR AU - Wu, Peng AU - Hurst, H. Jillian AU - French, Alexis AU - Chrestensen, Michael AU - Goldstein, A. Benjamin PY - 2025/3/4 TI - Linking Electronic Health Record Prescribing Data and Pharmacy Dispensing Records to Identify Patient-Level Factors Associated With Psychotropic Medication Receipt: Retrospective Study JO - JMIR Med Inform SP - e63740 VL - 13 KW - electronic health records KW - pharmacy dispensing KW - psychotropic medications KW - prescriptions KW - predictive modeling N2 - Background: Pharmacoepidemiology studies using electronic health record (EHR) data typically rely on medication prescriptions to determine which patients have received a medication. However, such data do not affirmatively indicate whether these prescriptions have been filled. External dispensing databases can bridge this information gap; however, few established methods exist for linking EHR data and pharmacy dispensing records. Objective: We described a process for linking EHR prescribing data with pharmacy dispensing records from Surescripts. As a use case, we considered the prescriptions and resulting fills for psychotropic medications among pediatric patients. We evaluated how dispensing information affects identifying patients receiving prescribed medications and assessing the association between filling prescriptions and subsequent health behaviors. Methods: This retrospective study identified all new psychotropic prescriptions to patients younger than 18 years of age at Duke University Health System in 2021. We linked dispensing to prescribing data using proximate dates and matching codes between RxNorm concept unique identifiers and National Drug Codes. We described demographic, clinical, and service use characteristics to assess differences between patients who did versus did not fill prescriptions. We fit a least absolute shrinkage and selection operator (LASSO) regression model to evaluate the predictability of a fill. We then fit time-to-event models to assess the association between whether a patient filled a prescription and a future provider visit. Results: We identified 1254 pediatric patients with a new psychotropic prescription. In total, 976 (77.8%) patients filled their prescriptions within 30 days of their prescribing encounters. Thus, we set 30 days as a cut point for defining a valid prescription fill. Patients who filled prescriptions differed from those who did not in several key factors. Those who did not fill had slightly higher BMIs, lived in more disadvantaged neighborhoods, were more likely to have public insurance or self-pay, and included a higher proportion of male patients. Patients with prior well-child visits or prescriptions from primary care providers were more likely to fill. Additionally, patients with anxiety diagnoses and those prescribed selective serotonin reuptake inhibitors were more likely to fill prescriptions. The LASSO model achieved an area under the receiver operator characteristic curve of 0.816. The time to the follow-up visit with the same provider was censored at 90 days after the initial encounter. Patients who filled prescriptions showed higher levels of follow-up visits. The marginal hazard ratio of a follow-up visit with the same provider was 1.673 (95% CI 1.463?1.913) for patients who filled their prescriptions. Using the LASSO model as a propensity-based weight, we calculated the weighted hazard ratio as 1.447 (95% CI 1.257?1.665). Conclusions: Systematic differences existed between patients who did versus did not fill prescriptions. Incorporating external dispensing databases into EHR-based studies informs medication receipt and associated health outcomes. UR - https://medinform.jmir.org/2025/1/e63740 UR - http://dx.doi.org/10.2196/63740 ID - info:doi/10.2196/63740 ER - TY - JOUR AU - Joshi, Aditya AU - Kaune, Federico Diego AU - Leff, Phillip AU - Fraser, Emily AU - Lee, Sarah AU - Harrison, Morgan AU - Hazin, Moustafa PY - 2025/2/18 TI - Self-Reported Side Effects Associated With Selective Androgen Receptor Modulators: Social Media Data Analysis JO - J Med Internet Res SP - e65031 VL - 27 KW - selective androgen receptor modulator KW - SARM KW - liver toxicity KW - social media KW - data analysis KW - anabolic KW - muscle KW - bone KW - toxicities KW - self-report KW - side effect KW - retrospective analysis KW - public post KW - Reddit KW - androgen receptor ligands KW - drug UR - https://www.jmir.org/2025/1/e65031 UR - http://dx.doi.org/10.2196/65031 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65031 ER - TY - JOUR AU - Insani, Norma Widya AU - Zakiyah, Neily AU - Puspitasari, Melyani Irma AU - Permana, Yorga Muhammad AU - Parmikanti, Kankan AU - Rusyaman, Endang AU - Suwantika, Abdurrohim Auliya PY - 2025/2/5 TI - Digital Health Technology Interventions for Improving Medication Safety: Systematic Review of Economic Evaluations JO - J Med Internet Res SP - e65546 VL - 27 KW - digital health technology KW - drug safety KW - adverse drug events KW - medication errors KW - patient safety N2 - Background: Medication-related harm, including adverse drug events (ADEs) and medication errors, represents a significant iatrogenic burden in clinical care. Digital health technology (DHT) interventions can significantly enhance medication safety outcomes. Although the clinical effectiveness of DHT for medication safety has been relatively well studied, much less is known about the cost-effectiveness of these interventions. Objective: This study aimed to systematically review the economic impact of DHT interventions on medication safety and examine methodological challenges to inform future research directions. Methods: A systematic search was conducted across 3 major electronic databases (ie, PubMed, Scopus, and EBSCOhost). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed for this systematic review. Two independent investigators conducted a full-text review after screening preliminary titles and abstracts. We adopted recommendations from the Panel on Cost-Effectiveness in Health and Medicine for data extraction. A narrative analysis was conducted to synthesize clinical and economic outcomes. The quality of reporting for the included studies was assessed using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) guidelines. Results: We included 13 studies that assessed the cost-effectiveness (n=9, 69.2%), cost-benefit (n=3, 23.1%), and cost-utility (n=1, 7.7%) of DHT for medication safety. Of the included studies, more than half (n=7, 53.9%) evaluated a clinical decision support system (CDSS)/computerized provider order entry (CPOE), 4 (30.8%) examined automated medication-dispensing systems, and 2 (15.4%) focused on pharmacist-led outreach programs targeting health care professionals. In 12 (92.3% ) studies, DHT was either cost-effective or cost beneficial compared to standard care. On average, DHT interventions reduced ADEs by 37.12% (range 8.2%-66.5%) and medication errors by 54.38% (range 24%-83%). The key drivers of cost-effectiveness included reductions in outcomes, the proportion of errors resulting in ADEs, and implementation costs. Despite a significant upfront cost, DHT showed a return on investment within 3-4.25 years due to lower cost related with ADE treatment and improved workflow efficiency. In terms of reporting quality, the studies were classified as good (n=10, 76.9%) and moderate (n=3, 23.1%). Key methodological challenges included short follow-up periods, the absence of alert compliance tracking, the lack of ADE and error severity categorization, and omission of indirect costs. Conclusions: DHT interventions are economically viable to improve medication safety, with a substantial reduction in ADEs and medication errors. Future studies should prioritize incorporating alert compliance tracking, ADE and error severity classification, and evaluation of indirect costs, thereby increasing clinical benefits and economic viability. UR - https://www.jmir.org/2025/1/e65546 UR - http://dx.doi.org/10.2196/65546 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65546 ER - TY - JOUR AU - Elphinston, A. Rachel AU - Pager, Sue AU - Brown, Kelly AU - Sterling, Michele AU - Fatehi, Farhad AU - Gray, Paul AU - Hipper, Linda AU - Cahill, Lauren AU - Connor, P. Jason PY - 2025/1/30 TI - Co-Designing a Digital Brief Intervention to Reduce the Risk of Prescription Opioid?Related Harm Among People With Chronic Noncancer Pain: Qualitative Analysis of Patient Lived Experiences JO - JMIR Form Res SP - e57208 VL - 9 KW - chronic noncancer pain KW - prescription opioid use KW - brief intervention KW - co-design KW - patient partners KW - lived experience KW - qualitative KW - digital health N2 - Background: Opioid medications are important for pain management, but many patients progress to unsafe medication use. With few personalized and accessible behavioral treatment options to reduce potential opioid-related harm, new and innovative patient-centered approaches are urgently needed to fill this gap. Objective: This study involved the first phase of co-designing a digital brief intervention to reduce the risk of opioid-related harm by investigating the lived experience of chronic noncancer pain (CNCP) in treatment-seeking patients, with a particular focus on opioid therapy experiences. Methods: Eligible patients were those aged between 18 and 70 years with CNCP at a clinically significant level of intensity (a score of ?4 of 10). Purposive sampling was used to engage patients on public hospital waitlists via mail or through the treating medical specialist. Participants (N=18; n=10 women; mean age 49.5 years, SD 11.50) completed semistructured telephone interviews. Interviews were transcribed verbatim, thematically analyzed using grounded theory, and member checked by patients. Results: Eight overarching themes were found, listed in the order of their prominence from most to least prominent: limited treatment collaboration and partnership; limited biopsychosocial understanding of pain; continued opioid use when benefits do not outweigh harms; a trial-and-error approach to opioid use; cycles of hopefulness and hopelessness; diagnostic uncertainty; significant negative impacts tied to loss; and complexity of pain and opioid use journeys. Conclusions: The findings of this study advance progress in co-designing digital brief interventions by actively engaging patient partners in their lived experiences of chronic pain and use of prescription opioid medications. The key recommendations proposed should guide the development of personalized solutions to address the complex care needs of patients with CNCP. UR - https://formative.jmir.org/2025/1/e57208 UR - http://dx.doi.org/10.2196/57208 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57208 ER - TY - JOUR AU - Gebreyohannes, Alemayehu Eyob AU - Thornton, Christopher AU - Thiessen, Myra AU - de Vries, T. Sieta AU - Q Andrade, Andre AU - Kalisch Ellett, Lisa AU - Frank, Oliver AU - Cheah, Yeong Phaik AU - Choo, Raymond Kim-Kwang AU - Laba, Lea Tracey AU - Roughead, E. Elizabeth AU - Hwang, Indae AU - Moses, Geraldine AU - Lim, Renly PY - 2025/1/15 TI - Co-Designing a Consumer-Focused Digital Reporting Health Platform to Improve Adverse Medicine Event Reporting: Protocol for a Multimethod Research Project (the ReMedi Project) JO - JMIR Res Protoc SP - e60084 VL - 14 KW - adverse drug events KW - drug-related side effects and adverse reactions KW - adverse drug reaction reporting systems KW - pharmacovigilance KW - digital health KW - medication safety KW - co-design KW - qualitative research, user-centered design N2 - Background: Adverse medicine events (AMEs) are unintended effects that occur following administration of medicines. Up to 70% of AMEs are not reported to, and hence remain undetected by, health care professionals and only 6% of AMEs are reported to regulators. Increased reporting by consumers, health care professionals, and pharmaceutical companies to medicine regulatory authorities is needed to increase the safety of medicines. Objective: We describe a project that aims to co-design a digital reporting platform to improve detection and management of AMEs by consumers and health care professionals and improve reporting to regulators. Methods: The project will be conducted in 3 phases and uses a co-design methodology that prioritizes equity in designing with stakeholders. Our project is guided by the Consolidated Framework for Implementation Research. In phase 1, we will engage with 3 stakeholder groups?consumers, health care professionals, and regulators?to define digital platform development standards. We will conduct a series of individual interviews, focus group discussions, and co-design workshops with the stakeholder groups. In phase 2, we will work with a software developer and user interaction design experts to prototype, test, and develop the digital reporting platform based on findings from phase 1. In phase 3, we will implement and trial the digital reporting platform in South Australia through general practices and pharmacies. Consumers who have recently started using medicines new to them will be recruited to use the digital reporting platform to report any apparent, suspected, or possible AMEs since starting the new medicine. Process and outcome evaluations will be conducted to assess the implementation process and to determine whether the new platform has increased AME detection and reporting. Results: This project, initiated in 2023, will run until 2026. Phase 1 will result in persona profiles and user journey maps that define the standards for the user-friendly platform and interactive data visualization tool or dashboard that will be developed and further improved in phase 2. Finally, phase 3 will provide insights of the implemented platform regarding its impact on AME detection, management, and reporting. Findings will be published progressively as we complete the different phases of the project. Conclusions: This project adopts a co-design methodology to develop a new digital reporting platform for AME detection and reporting, considering the perspectives and lived experience of stakeholders and addressing their requirements throughout the entire process. The overarching goal of the project is to leverage the potential of both consumers and technology to address the existing challenges of underdetection and underreporting of AMEs to health care professionals and regulators. The project potentially will improve individual patient safety and generate new data for regulatory purposes related to medicine safety and effectiveness. International Registered Report Identifier (IRRID): DERR1-10.2196/60084 UR - https://www.researchprotocols.org/2025/1/e60084 UR - http://dx.doi.org/10.2196/60084 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60084 ER - TY - JOUR AU - Scharf, Tamara AU - Huber, A. Carola AU - Näpflin, Markus AU - Zhang, Zhongxing AU - Khatami, Ramin PY - 2025/1/7 TI - Trends in Prescription of Stimulants and Narcoleptic Drugs in Switzerland: Longitudinal Health Insurance Claims Analysis for the Years 2014-2021 JO - JMIR Public Health Surveill SP - e53957 VL - 11 KW - prescription trends KW - claims data KW - cross-sectional data KW - narcolepsy KW - prescribers KW - prescribing practices KW - medical care KW - stimulants KW - stimulant medication N2 - Background: Stimulants are potent treatments for central hypersomnolence disorders or attention-deficit/hyperactivity disorders/attention deficit disorders but concerns have been raised about their potential negative consequences and their increasing prescription rates. Objective: We aimed to describe stimulant prescription trends in Switzerland from 2014 to 2021. Second, we aimed to analyze the characteristics of individuals who received stimulant prescriptions in 2021 and investigate the link between stimulant prescriptions and hospitalization rates in 2021, using hospitalization as a potential indicator of adverse health outcomes. Methods: Longitudinal and cross-sectional data from a large Swiss health care insurance were analyzed from all insureds older than 6 years. The results were extrapolated to the Swiss general population. We identified prescriptions for methylphenidate, lisdexamfetamine, modafinil, and sodium oxybate and calculated prevalences of each drug prescription over the period from 2014 to 2021. For 2021 we provide detailed information on the prescribers and evaluate the association of stimulant prescription and the number and duration of hospitalization using logistic regression models. Results: We observed increasing prescription rates of all stimulants in all age groups from 2014 to 2021 (0.55% to 0.81%, 43,848 to 66,113 insureds with a prescription). In 2021, 37.1% (28,057 prescriptions) of the medications were prescribed by psychiatrists, followed by 36.1% (n=27,323) prescribed by general practitioners and 1% (n=748) by neurologists. Only sodium oxybate, which is highly specific for narcolepsy treatment, was most frequently prescribed by neurologists (27.8%, 37 prescriptions). Comorbid psychiatric disorders were common in patients receiving stimulants. Patients hospitalized in a psychiatric institution were 5.3 times (odds ratio 5.3, 95% CI 4.63?6.08, P<.001) more likely to have a stimulant prescription than those without hospitalization. There were no significant associations between stimulant prescription and the total length of inpatient stay (odds ratio 1, 95% CI 1?1, P=.13). Conclusions: The prescription of stimulant medication in Switzerland increased slightly but continuously over years, but at lower rates compared to the estimated prevalence of central hypersomnolence disorders and attention-deficit/hyperactivity disorders/attention deficit disorders. Most stimulants are prescribed by psychiatrists, closely followed by general practitioners. The increased odds for hospitalization to psychiatric institutions for stimulant receivers reflects the severity of disease and the higher psychiatric comorbidities in these patients. UR - https://publichealth.jmir.org/2025/1/e53957 UR - http://dx.doi.org/10.2196/53957 ID - info:doi/10.2196/53957 ER - TY - JOUR AU - Cheng, Yong Huai PY - 2025/1/3 TI - ChatGPT?s Attitude, Knowledge, and Clinical Application in Geriatrics Practice and Education: Exploratory Observational Study JO - JMIR Form Res SP - e63494 VL - 9 KW - ChatGPT KW - geriatrics attitude KW - ageism KW - geriatrics competence KW - geriatric syndromes KW - polypharmacy KW - falls KW - aging, older adults N2 - Background: The increasing use of ChatGPT in clinical practice and medical education necessitates the evaluation of its reliability, particularly in geriatrics. Objective: This study aimed to evaluate ChatGPT?s trustworthiness in geriatrics through 3 distinct approaches: evaluating ChatGPT?s geriatrics attitude, knowledge, and clinical application with 2 vignettes of geriatric syndromes (polypharmacy and falls). Methods: We used the validated University of California, Los Angeles, geriatrics attitude and knowledge instruments to evaluate ChatGPT?s geriatrics attitude and knowledge and compare its performance with that of medical students, residents, and geriatrics fellows from reported results in the literature. We also evaluated ChatGPT?s application to 2 vignettes of geriatric syndromes (polypharmacy and falls). Results: The mean total score on geriatrics attitude of ChatGPT was significantly lower than that of trainees (medical students, internal medicine residents, and geriatric medicine fellows; 2.7 vs 3.7 on a scale from 1-5; 1=strongly disagree; 5=strongly agree). The mean subscore on positive geriatrics attitude of ChatGPT was higher than that of the trainees (medical students, internal medicine residents, and neurologists; 4.1 vs 3.7 on a scale from 1 to 5 where a higher score means a more positive attitude toward older adults). The mean subscore on negative geriatrics attitude of ChatGPT was lower than that of the trainees and neurologists (1.8 vs 2.8 on a scale from 1 to 5 where a lower subscore means a less negative attitude toward aging). On the University of California, Los Angeles geriatrics knowledge test, ChatGPT outperformed all medical students, internal medicine residents, and geriatric medicine fellows from validated studies (14.7 vs 11.3 with a score range of ?18 to +18 where +18 means that all questions were answered correctly). Regarding the polypharmacy vignette, ChatGPT not only demonstrated solid knowledge of potentially inappropriate medications but also accurately identified 7 common potentially inappropriate medications and 5 drug-drug and 3 drug-disease interactions. However, ChatGPT missed 5 drug-disease and 1 drug-drug interaction and produced 2 hallucinations. Regarding the fall vignette, ChatGPT answered 3 of 5 pretests correctly and 2 of 5 pretests partially correctly, identified 6 categories of fall risks, followed fall guidelines correctly, listed 6 key physical examinations, and recommended 6 categories of fall prevention methods. Conclusions: This study suggests that ChatGPT can be a valuable supplemental tool in geriatrics, offering reliable information with less age bias, robust geriatrics knowledge, and comprehensive recommendations for managing 2 common geriatric syndromes (polypharmacy and falls) that are consistent with evidence from guidelines, systematic reviews, and other types of studies. ChatGPT?s potential as an educational and clinical resource could significantly benefit trainees, health care providers, and laypeople. Further research using GPT-4o, larger geriatrics question sets, and more geriatric syndromes is needed to expand and confirm these findings before adopting ChatGPT widely for geriatrics education and practice. UR - https://formative.jmir.org/2025/1/e63494 UR - http://dx.doi.org/10.2196/63494 UR - http://www.ncbi.nlm.nih.gov/pubmed/39752214 ID - info:doi/10.2196/63494 ER - TY - JOUR AU - Daluwatte, Chathuri AU - Khromava, Alena AU - Chen, Yuning AU - Serradell, Laurence AU - Chabanon, Anne-Laure AU - Chan-Ou-Teung, Anthony AU - Molony, Cliona AU - Juhaeri, Juhaeri PY - 2024/12/20 TI - Application of a Language Model Tool for COVID-19 Vaccine Adverse Event Monitoring Using Web and Social Media Content: Algorithm Development and Validation Study JO - JMIR Infodemiology SP - e53424 VL - 4 KW - adverse event KW - COVID-19 KW - detection KW - large language model KW - mass vaccination KW - natural language processing KW - pharmacovigilance KW - safety KW - social media KW - vaccine N2 - Background: Spontaneous pharmacovigilance reporting systems are the main data source for signal detection for vaccines. However, there is a large time lag between the occurrence of an adverse event (AE) and the availability for analysis. With global mass COVID-19 vaccination campaigns, social media, and web content, there is an opportunity for real-time, faster monitoring of AEs potentially related to COVID-19 vaccine use. Our work aims to detect AEs from social media to augment those from spontaneous reporting systems. Objective: This study aims to monitor AEs shared in social media and online support groups using medical context-aware natural language processing language models. Methods: We developed a language model?based web app to analyze social media, patient blogs, and forums (from 190 countries in 61 languages) around COVID-19 vaccine?related keywords. Following machine translation to English, lay language safety terms (ie, AEs) were observed using the PubmedBERT-based named-entity recognition model (precision=0.76 and recall=0.82) and mapped to Medical Dictionary for Regulatory Activities (MedDRA) terms using knowledge graphs (MedDRA terminology is an internationally used set of terms relating to medical conditions, medicines, and medical devices that are developed and registered under the auspices of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use). Weekly and cumulative aggregated AE counts, proportions, and ratios were displayed via visual analytics, such as word clouds. Results: Most AEs were identified in 2021, with fewer in 2022. AEs observed using the web app were consistent with AEs communicated by health authorities shortly before or within the same period. Conclusions: Monitoring the web and social media provides opportunities to observe AEs that may be related to the use of COVID-19 vaccines. The presented analysis demonstrates the ability to use web content and social media as a data source that could contribute to the early observation of AEs and enhance postmarketing surveillance. It could help to adjust signal detection strategies and communication with external stakeholders, contributing to increased confidence in vaccine safety monitoring. UR - https://infodemiology.jmir.org/2024/1/e53424 UR - http://dx.doi.org/10.2196/53424 UR - http://www.ncbi.nlm.nih.gov/pubmed/39705077 ID - info:doi/10.2196/53424 ER - TY - JOUR AU - Takura, Tomoyuki AU - Yokoi, Hiroyoshi AU - Honda, Asao PY - 2024/12/6 TI - Factors Influencing Drug Prescribing for Patients With Hospitalization History in Circulatory Disease?Patient Severity, Composite Adherence, and Physician-Patient Relationship: Retrospective Cohort Study JO - JMIR Aging SP - e59234 VL - 7 KW - medication adherence KW - drug prescription switch KW - generic drug KW - logistic model KW - long-term longitudinal study KW - patient severity KW - systolic blood pressure KW - serum creatinine KW - aging KW - big data N2 - Background: With countries promoting generic drug prescribing, their growth may plateau, warranting further investigation into the factors influencing this trend, including physician and patient perspectives. Additional strategies may be needed to maximize the switch to generic drugs while ensuring health care system sustainability, focusing on factors beyond mere low cost. Emphasizing affordability and clarifying other prescription considerations are essential. Objective: This study aimed to provide initial insights into how patient severity, composite adherence, and physician-patient relationships impact generic switching. Methods: This study used a long-term retrospective cohort design by analyzing data from a national health care database. The population included patients of all ages, primarily older adults, who required primary-to-tertiary preventive actions with a history of hospitalization for cardiovascular diseases (ICD-10 [International Statistical Classification of Diseases, Tenth Revision]) from April 2014 to March 2018 (4 years). We focused on switching to generic drugs, with temporal variations in clinical parameters as independent variables. Lifestyle factors (smoking and drinking) were also considered. Adherence was measured as a composite score comprising 11 elements. The physician-patient relationship was established based on the interval between physician change and prescription. Logistic regression analysis and propensity score matching were used, along with complementary analysis of physician-patient relationships, proportion of days covered, and adherence for a subset of the population. Results: The study included 48,456 patients with an average follow-up of 36.1 (SD 8.8) months. The mean age was 68.3?(SD 9.9)?years; BMI, 23.4?(SD?3.4)?kg/m2; systolic blood pressure, 131.2?(SD?15)?mm Hg; low-density lipoprotein cholesterol level, 116.6?(SD?29.3)?mg/dL; hemoglobin A1c (HbA1c), 5.9%?(SD?0.8%); and serum creatinine level, 0.9?(SD?0.8)?mg/dL. Logistic regression analysis revealed significant associations between generic switching and systolic blood pressure (odds ratio [OR] 0.996, 95% CI 0.993-0.999), serum creatinine levels (OR 0.837, 95% CI 0.729-0.962), glutamic oxaloacetic transaminase levels (OR 0.994, 95% CI 0.990-0.997), proportion of days covered score (OR 0.959, 95% CI 0.948-0.97), and adherence score (OR 0.910, 95% CI 0.875-0.947). In addition, generic drug rates increased with improvements in the HbA1c level band and smoking level (P<.01 and P<.001). The group with a superior physician-patient relationship after propensity score matching had a significantly higher rate of generic drug prescribing (51.6%, SD 15.2%) than the inferior relationship group (47.7%, SD17.7%; P<.001). Conclusions: Although physicians? understanding influences the choice of generic drugs, patient condition (severity) and adherence also impact this decision. For example, improved creatinine levels are associated with generic drug choice, while stronger physician-patient relationships correlate with higher rates of generic drug use. These findings may contribute to the appropriate prescription of pharmaceuticals if the policy diffusion of generic drugs begins to slow down. Thus, preventing serious illness while building trust may result in clinical benefits and positive socioeconomic outcomes. UR - https://aging.jmir.org/2024/1/e59234 UR - http://dx.doi.org/10.2196/59234 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59234 ER - TY - JOUR AU - Gosselin, Laura AU - Maes, Alexandre AU - Eyer, Kevin AU - Dahamna, Badisse AU - Disson, Flavien AU - Darmoni, Stefan AU - Wils, Julien AU - Grosjean, Julien PY - 2024/11/28 TI - Design and Implementation of a Dashboard for Drug Interactions Mediated by Cytochromes Using a Health Care Data Warehouse in a University Hospital Center: Development Study JO - JMIR Med Inform SP - e57705 VL - 12 KW - drug-drug interaction KW - adverse KW - interaction KW - information system KW - warehouse KW - warehousing KW - cytochrome KW - personalized medicine KW - dashboard KW - drugs KW - pharmacy KW - pharmacology KW - pharmacotherapy KW - pharmaceutic KW - pharmaceutical KW - medication KW - visualization KW - develop KW - development KW - design N2 - Background: The enzymatic system of cytochrome P450 (CYP450) is a group of enzymes involved in the metabolism of drugs present in the liver. Literature records instances of underdosing of drugs due to the concurrent administration of another drug that strongly induces the same cytochrome for which the first drug is a substrate and overdosing due to strong inhibition. IT solutions have been proposed to raise awareness among prescribers to mitigate these interactions. Objective: This study aimed to develop a drug interaction dashboard for Cytochrome-mediated drug interactions (DIDC) using a health care data warehouse to display results that are easily readable and interpretable by clinical experts. Methods: The initial step involved defining requirements with expert pharmacologists. An existing model of interactions involving the (CYP450) was used. A program for the automatic detection of cytochrome-mediated drug interactions (DI) was developed. Finally, the development and visualization of the DIDC were carried out by an IT engineer. An evaluation of the tool was carried out. Results: The development of the DIDC was successfully completed. It automatically compiled cytochrome-mediated DIs in a comprehensive table and provided a dedicated dashboard for each potential DI. The most frequent interaction involved paracetamol and carbamazepine with CYP450 3A4 (n=50 patients). The prescription of tacrolimus with CYP3A5 genotyping pertained to 675 patients. Two experts qualitatively evaluated the tool, resulting in overall satisfaction scores of 6 and 5 out of 7, respectively. Conclusions: At our hospital, measurements of molecules that could have altered concentrations due to cytochrome-mediated DIs are not systematic. These DIs can lead to serious clinical consequences. The purpose of this DIDC is to provide an overall view and raise awareness among prescribers about the importance of measuring concentrations of specific drugs and metabolites. Ultimately, the tool could lead to an individualized approach and become a prescription support tool if integrated into prescription assistance software. UR - https://medinform.jmir.org/2024/1/e57705 UR - http://dx.doi.org/10.2196/57705 ID - info:doi/10.2196/57705 ER - TY - JOUR AU - Lee, Chung-Chun AU - Lee, Seunghee AU - Song, Mi-Hwa AU - Kim, Jong-Yeup AU - Lee, Suehyun PY - 2024/11/20 TI - Bidirectional Long Short-Term Memory?Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches JO - JMIR Med Inform SP - e45289 VL - 12 KW - adverse drug reaction KW - social network service KW - classification model KW - Korean text data KW - social networking service KW - drug detection KW - deep learning KW - Korea KW - social data KW - older KW - older adults KW - drug surveillance KW - medicine N2 - Background: Social networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to detect adverse drug reactions (ADRs) early using drug surveillance systems. To this end, various deep learning methods have been used to analyze data in multiple languages in addition to English. Objective: A cautionary drug that can cause ADRs in older patients was selected, and Korean SNS data containing this drug information were collected. Based on this information, we aimed to develop a deep learning model that classifies drug ADR posts based on a recurrent neural network. Methods: In previous studies, ketoprofen, which has a high prescription frequency and, thus, was referred to the most in posts secured from SNS data, was selected as the target drug. Blog posts, café posts, and NAVER Q&A posts from 2005 to 2020 were collected from NAVER, a portal site containing drug-related information, and natural language processing techniques were applied to analyze data written in Korean. Posts containing highly relevant drug names and ADR word pairs were filtered through association analysis, and training data were generated through manual labeling tasks. Using the training data, an embedded layer of word2vec was formed, and a Bidirectional Long Short-Term Memory (Bi-LSTM) classification model was generated. Then, we evaluated the area under the curve with other machine learning models. In addition, the entire process was further verified using the nonsteroidal anti-inflammatory drug aceclofenac. Results: Among the nonsteroidal anti-inflammatory drugs, Korean SNS posts containing information on ketoprofen and aceclofenac were secured, and the generic name lexicon, ADR lexicon, and Korean stop word lexicon were generated. In addition, to improve the accuracy of the classification model, an embedding layer was created considering the association between the drug name and the ADR word. In the ADR post classification test, ketoprofen and aceclofenac achieved 85% and 80% accuracy, respectively. Conclusions: Here, we propose a process for developing a model for classifying ADR posts using SNS data. After analyzing drug name-ADR patterns, we filtered high-quality data by extracting posts, including known ADR words based on the analysis. Based on these data, we developed a model that classifies ADR posts. This confirmed that a model that can leverage social data to monitor ADRs automatically is feasible. UR - https://medinform.jmir.org/2024/1/e45289 UR - http://dx.doi.org/10.2196/45289 ID - info:doi/10.2196/45289 ER - TY - JOUR AU - Zhang, Pengfei AU - Kamitaki, K. Brad AU - Do, Phu Thien PY - 2024/11/8 TI - Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene?Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media JO - JMIR Form Res SP - e58176 VL - 8 KW - internet KW - patient reported outcome KW - headache KW - health information KW - Reddit KW - registry KW - monoclonal antibody KW - crowdsourcing KW - postmarketing KW - safety KW - surveillance KW - migraine KW - preventives KW - prevention KW - self-reported KW - calcitonin gene?related peptide KW - calcitonin KW - therapeutics KW - social media KW - medication-related KW - posts KW - propranolol KW - topiramate KW - erenumab KW - fremanezumab KW - cross-sectional KW - surveys N2 - Background: Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene?related peptide (CGRP) signaling for migraine prevention. However, these trials may not accurately reflect the real-world experiences of more diverse and heterogeneous patient populations, who often have higher disease burden and more comorbidities. Therefore, postmarketing safety surveillance is warranted. Regulatory organizations encourage marketing authorization holders to screen digital media for suspected adverse reactions, applying the same requirements as for spontaneous reports. Real-world data from social media platforms constitute a potential venue to capture diverse patient experiences and help detect treatment-related adverse events. However, while social media holds promise for this purpose, its use in pharmacovigilance is still in its early stages. Computational linguistics, which involves the automatic manipulation and quantitative analysis of oral or written language, offers a potential method for exploring this content. Objective: This study aims to characterize adverse events related to monoclonal antibodies targeting CGRP signaling on Reddit, a large online social media forum, by using computational linguistics. Methods: We examined differences in word frequencies from medication-related posts on the Reddit subforum r/Migraine over a 10-year period (2010-2020) using computational linguistics. The study had 2 phases: a validation phase and an application phase. In the validation phase, we compared posts about propranolol and topiramate, as well as posts about each medication against randomly selected posts, to identify known and expected adverse events. In the application phase, we analyzed posts discussing 2 monoclonal antibodies targeting CGRP signaling?erenumab and fremanezumab?to identify potential adverse events for these medications. Results: From 22,467 Reddit r/Migraine posts, we extracted 402 (2%) propranolol posts, 1423 (6.33%) topiramate posts, 468 (2.08%) erenumab posts, and 73 (0.32%) fremanezumab posts. Comparing topiramate against propranolol identified several expected adverse events, for example, ?appetite,? ?weight,? ?taste,? ?foggy,? ?forgetful,? and ?dizziness.? Comparing erenumab against a random selection of terms identified ?constipation? as a recurring keyword. Comparing erenumab against fremanezumab identified ?constipation,? ?depression,? ?vomiting,? and ?muscle? as keywords. No adverse events were identified for fremanezumab. Conclusions: The validation phase of our study accurately identified common adverse events for oral migraine preventive medications. For example, typical adverse events such as ?appetite? and ?dizziness? were mentioned in posts about topiramate. When we applied this methodology to monoclonal antibodies targeting CGRP or its receptor?fremanezumab and erenumab, respectively?we found no definite adverse events for fremanezumab. However, notable flagged words for erenumab included ?constipation,? ?depression,? and ?vomiting.? In conclusion, computational linguistics applied to social media may help identify potential adverse events for novel therapeutics. While social media data show promise for pharmacovigilance, further work is needed to improve its reliability and usability. UR - https://formative.jmir.org/2024/1/e58176 UR - http://dx.doi.org/10.2196/58176 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58176 ER - TY - JOUR AU - Smith, N. Shawna AU - Lanham, M. Michael S. AU - Seagull, Jacob F. AU - Fabbri, Morris AU - Dorsch, P. Michael AU - Jennings, Kathleen AU - Barnes, Geoffrey PY - 2024/11/8 TI - System-Wide, Electronic Health Record?Based Medication Alerts for Appropriate Prescribing of Direct Oral Anticoagulants: Pilot Randomized Controlled Trial JO - JMIR Form Res SP - e64674 VL - 8 KW - direct oral anticoagulants KW - electronic health record KW - medication safety KW - prescribing errors KW - pilot randomized controlled trial KW - alert system optimization KW - clinical decision support KW - EHR KW - randomized controlled trial KW - RCT KW - oral anticoagulants N2 - Background: While direct oral anticoagulants (DOACs) have improved oral anticoagulation management, inappropriate prescribing remains prevalent and leads to adverse drug events. Antithrombotic stewardship programs seek to enhance DOAC prescribing but require scalable and sustainable strategies. Objective: We present a pilot, prescriber-level randomized controlled trial to assess the effectiveness of electronic health record (EHR)?based medication alerts in a large health system. Methods: The pilot assessed prescriber responses to alerts for initial DOAC prescription errors (apixaban and rivaroxaban). A user-centered, multistage design process informed alert development, emphasizing clear indication, appropriate dosing based on renal function, and drug-drug interactions. Alerts appeared whenever a DOAC was being prescribed in a way that did not follow package label instructions. Clinician responses measured acceptability, accuracy, feasibility, and utilization of the alerts. Results: The study ran from August 1, 2022, through April 30, 2023. Only 1 prescriber requested trial exclusion, demonstrating acceptability. The error rate for false alerts due to incomplete data was 6.6% (16/243). Two scenarios with alert design and/or execution errors occurred but were quickly identified and resolved, underlining the importance of a responsive quality assurance process in EHR-based interventions. Trial feasibility issues related to alert-data capture were identified and resolved. Trial feasibility was also assessed with balanced randomization of prescribers and the inclusion of various alerts across both medications. Assessing utilization, 34.2% (83/243) of the encounters (with 134 prescribers) led to a prescription change. Conclusions: The pilot implementation study demonstrated the acceptability, accuracy, feasibility, and estimates of the utilization of EHR-based medication alerts for DOAC prescriptions and successfully established just-in-time randomization of prescribing clinicians. This pilot study sets the stage for large-scale, randomized implementation evaluations of EHR-based alerts to improve medication safety. Trial Registration: ClinicalTrials.gov NCT05351749; https://clinicaltrials.gov/study/NCT05351749 UR - https://formative.jmir.org/2024/1/e64674 UR - http://dx.doi.org/10.2196/64674 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64674 ER - TY - JOUR AU - Gustafson Hedov, Emelie AU - Nyberg, Fredrik AU - Gustafsson, Stefan AU - Li, Huiqi AU - Gisslén, Magnus AU - Sundström, Johan PY - 2024/10/30 TI - Person-Centered Web-Based Mobile Health System (Symptoms) for Reporting Symptoms in COVID-19 Vaccinated Individuals: Observational Study of System, Users, and Symptoms JO - JMIR Form Res SP - e57514 VL - 8 KW - mHealth KW - mobile health KW - patient-reported outcomes KW - apps KW - COVID-19 KW - vaccination side effects KW - web-based symptom reporting N2 - Background: The full spectrum of side effects from COVID-19 vaccinations and infections, including milder symptoms or health effects that do not lead to health care visits, remains unknown. Person-centered self-reporting of symptoms may offer a solution. Monitoring patient-reported outcomes over time will vary in importance for different patients. Individuals have unique needs and preferences, in terms of both communication methods and how the collected information is used to support care. Objective: This study aims to describe how Symptoms, a system for person-centered self-reporting of symptoms and health-related quality of life, was utilized in investigating COVID-19 vaccine side effects. We illustrate this by presenting data from the Symptoms system in newly vaccinated individuals from the RECOVAC (Register-based large-scale national population study to monitor COVID-19 vaccination effectiveness and safety) study. Methods: During the COVID-19 pandemic, newly vaccinated individuals were identified as the ideal population to query for milder symptoms related to COVID-19 vaccinations and infections. To this end, we used posters in observation areas at 150 vaccination sites across the Västra Götaland region of Sweden, inviting newly vaccinated individuals to use a novel digital system, Symptoms. In the Symptoms system, users can track their symptoms, functioning, and quality of life as often as they wish, using evidence-based patient-reported outcome measures and short numeric rating scales. These scales cover a prespecified list of symptoms based on common COVID-19 symptoms and previously reported vaccine side effects. Participants could also use numeric rating scales for self-defined symptoms if their symptom was not included on the prespecified list. Results: A total of 731 people created user accounts and consented to share data for research between July 21, 2021, and September 27, 2022. The majority of users were female (444/731, 60.7%), with a median age of 38 (IQR 30-47) years. Most participants (498/702, 70.9%) did not report any of the comorbidities included in the questionnaire. Of the 731 participants, 563 (77.0%) reported experiencing 1 or more symptoms. The most common symptom was pain at the injection site (486/563, 86.3%), followed by fatigue (181/563, 32.1%) and headache (169/563, 30.0%). In total, 143 unique symptoms were reported. Of these, 29 were from the prespecified list, while the remaining 114 (79.7%) were self-defined entries in the symptom field. This suggests that the flexibility of the self-directed system?allowing individuals to decide which symptoms they consider worth tracking?may be an important feature. Conclusions: Self-reported symptoms in the Symptoms system appeared to align with previously observed post?COVID-19 vaccination symptoms. The system was relatively easy to use and successfully captured broad, longitudinal data. Its person-centered and self-directed design seemed crucial in capturing the full burden of symptoms experienced by users. UR - https://formative.jmir.org/2024/1/e57514 UR - http://dx.doi.org/10.2196/57514 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57514 ER - TY - JOUR AU - Nishiyama, Tomohiro AU - Yamaguchi, Ayane AU - Han, Peitao AU - Pereira, Kanashiro Lis Weiji AU - Otsuki, Yuka AU - Andrade, Bernardim Gabriel Herman AU - Kudo, Noriko AU - Yada, Shuntaro AU - Wakamiya, Shoko AU - Aramaki, Eiji AU - Takada, Masahiro AU - Toi, Masakazu PY - 2024/9/24 TI - Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study JO - JMIR Med Inform SP - e58977 VL - 12 KW - natural language processing KW - named entity recognition KW - adverse drug reaction KW - adverse event KW - peripheral neuropathy KW - NLP KW - symptoms KW - symptom KW - machine learning KW - ML KW - drug KW - drugs KW - pharmacology KW - pharmacotherapy KW - pharmaceutic KW - pharmaceutics KW - pharmaceuticals KW - pharmaceutical KW - medication KW - medications KW - adverse KW - neuropathy KW - cancer KW - oncology KW - text KW - texts KW - textual KW - note KW - notes KW - report KW - reports KW - EHR KW - EHRs KW - record KW - records KW - detect KW - detection KW - detecting N2 - Background: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient?s status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Objective: This study aimed to investigate the system?s performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system. Methods: We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration. Results: Our system underestimates by 13.3 percentage points (74.0%?60.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician?s progress notes, followed by the pharmacist?s and nursing records. Conclusions: Considering the inherent cost that requires constant monitoring of the patient?s condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents. UR - https://medinform.jmir.org/2024/1/e58977 UR - http://dx.doi.org/10.2196/58977 UR - http://www.ncbi.nlm.nih.gov/pubmed/39316418 ID - info:doi/10.2196/58977 ER - TY - JOUR AU - Oluokun, Oluwatosin Emmanuel AU - Adedoyin, Fatai Festus AU - Dogan, Huseyin AU - Jiang, Nan PY - 2024/9/12 TI - Co-Designing Digital Health Intervention for Monitoring Medication and Consultation Among Transgender People in Underserved Communities: Collaborative Approach JO - JMIR Hum Factors SP - e45826 VL - 11 KW - digital health KW - HIV/AIDS medication KW - data-driven health care KW - ART KW - transgender KW - LGTBQI+ KW - interactive management N2 - Background: In many parts of the world, men who have sex with men and transgender individuals face criminalization and discrimination. As a result, they are less likely to seek medical help, despite experiencing higher rates of HIV/AIDS, mental health issues, and other health problems. Reaching key populations (KPs) with essential testing, care, and treatment services can be challenging, as they often have a higher likelihood of contracting and spreading the virus. They have limited access to antiretroviral (ARV) therapy (ART) services, which means that KPs may continue to serve as reservoirs for new HIV infections if they do not receive effective HIV programming. This ongoing issue complicates efforts to control the epidemic. Therefore, modeling a digital health system to track ARV medication access and use is crucial. This paper advocates for the use of digital interventions to manage the health of KPs in underserved regions, using Nigeria as a case study. Objective: This study aims to assess digital health interventions for monitoring medication and consultations among transgender people in underserved communities. It also sought to determine whether a system exists that could support ART adherence in Nigeria. Additionally, the study evaluated design strategies to address privacy and confidentiality concerns, aiming to reduce nonadherence to ARV medications among KPs in Nigeria. Methods: A qualitative approach was adopted for this research, involving a thematic analysis of information collected from interviews with clinicians and other health practitioners who work directly with these communities, as well as from an interactive (virtual) workshop. Results: The findings from the thematic analysis indicate a need to increase attendance at ART therapy sessions through the implementation of an intensive care web app. Unlike previous solutions, this study highlights the importance of incorporating a reminder feature that integrates with an in-app telemedicine consultancy platform. This platform would facilitate discussions about client challenges, such as adverse drug effects, counseling sessions with clinical psychologists, and the impact of identity discrimination on mental health. Other data-driven health needs identified in the study are unique drug request nodes, client-led viral load calculators, remote requests, and drug delivery features within the web app. Participants also emphasized the importance of monitoring medication compliance and incorporating user feedback mechanisms, such as ratings and encouragement symbols (eg, stars, checkmarks), to motivate adherence. Conclusions: The study concludes that technology-driven solutions could enhance ART adherence and reduce HIV transmission among transgender people. It also recommends that local governments and international organizations collaborate and invest in health management services that prioritize health needs over identity. UR - https://humanfactors.jmir.org/2024/1/e45826 UR - http://dx.doi.org/10.2196/45826 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/45826 ER - TY - JOUR AU - Tas, Basak AU - Lawn, Will AU - Jauncey, Marianne AU - Bartlett, Mark AU - Dietze, Paul AU - O'Keefe, Daniel AU - Clark, Nico AU - Henderson, Bruce AU - Cowan, Catriona AU - Meredith, Osian AU - Strang, John PY - 2024/9/10 TI - Overdose Detection Among High-Risk Opioid Users Via a Wearable Chest Sensor in a Supervised Injecting Facility: Protocol for an Observational Study JO - JMIR Res Protoc SP - e57367 VL - 13 KW - wearable sensor KW - overdose KW - opioid-related deaths KW - injecting opioid use KW - medically supervised injection center KW - opioid induced respiratory depression KW - mobile phone KW - opioid overdose KW - drug overdose KW - Australia KW - United States KW - chest biosensor KW - biosensor KW - wearable device KW - respiratory depression KW - algorithm KW - detection algorithm KW - observational design KW - illicit drugs KW - safe injecting facilities KW - naloxone KW - wearable N2 - Background: Opioid overdose is a global health crisis, affecting over 27 million individuals worldwide, with more than 100,000 drug overdose deaths in the United States in 2022-2023. This protocol outlines the development of the PneumoWave chest biosensor, a wearable device being designed to detect respiratory depression in real time through chest motion measurement, intending to enhance early intervention and thereby reduce fatalities. Objective: The study aims to (1) differentiate opioid-induced respiratory depression (OIRD) from nonfatal opioid use patterns to develop and refine an overdose detection algorithm and (2) examine participants? acceptability of the chest biosensor. Methods: The study adopts an observational design over a 6-month period. The biosensor, a small device, will be worn by consenting participants during injecting events to capture chest motion data. Safe injecting facilities (SIF) in Melbourne, Victoria (site 1), and Sydney, New South Wales (site 2), which are legally sanctioned spaces where individuals can use preobtained illicit drugs under medical supervision. Each site is anticipated to recruit up to 100 participants who inject opioids and attend the SIF. Participants will wear the biosensor during supervised injecting events at both sites. The biosensor will attempt to capture data on an anticipated 40 adverse drug events. The biosensor?s ability to detect OIRD will be compared to the staff-identified events that use standard protocols for managing overdoses. Measurements will include (1) chest wall movement measured by the biosensor, securely streamed to a cloud, and analyzed to refine an overdose detection algorithm and (2) acute events or potential overdose identified by site staff. Acceptability will be measured by a feedback questionnaire as many times as the participant is willing to throughout the study. Results: As of April 2024, a total of 47 participants have been enrolled and data from 1145 injecting events have already been collected, including 10 overdose events. This consists of 17 females and 30 males with an average age of 45 years. Data analysis is ongoing. Conclusions: This protocol establishes a foundation for advancing wearable technology in opioid overdose prevention within SIFs. The study will provide chest wall movement data and associated overdose data that will be used to train an algorithm that allows the biosensor to detect an overdose. The study will contribute crucial insights into OIRD, emphasizing the biosensor?s potential step forward in real-time intervention strategies. International Registered Report Identifier (IRRID): DERR1-10.2196/57367 UR - https://www.researchprotocols.org/2024/1/e57367 UR - http://dx.doi.org/10.2196/57367 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57367 ER - TY - JOUR AU - Golder, Su AU - O'Connor, Karen AU - Wang, Yunwen AU - Klein, Ari AU - Gonzalez Hernandez, Graciela PY - 2024/9/6 TI - The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review JO - JMIR Public Health Surveill SP - e59167 VL - 10 KW - adverse events KW - pharmacovigilance KW - social media KW - real-world data KW - scoping review N2 - Background: Adverse drug events pose an enormous public health burden, leading to hospitalization, disability, and death. Even the adverse events (AEs) categorized as nonserious can severely impact on patient?s quality of life, adherence, and persistence. Monitoring medication safety is challenging. Web-based patient reports on social media may be a useful supplementary source of real-world data. Despite the growth of sophisticated techniques for identifying AEs using social media data, a consensus has not been reached as to the value of social media in relation to more traditional data sources. Objective: This study aims to evaluate and characterize the utility of social media analysis in adverse drug event detection and pharmacovigilance as compared with other data sources (such as spontaneous reporting systems and the clinical literature). Methods: In this scoping review, we searched 11 bibliographical databases and Google Scholar, followed by handsearching and forward and backward citation searching. Each record was screened by 2 independent reviewers at both the title and abstract stage and the full-text screening stage. Studies were included if they used any type of social media (such as Twitter or patient forums) to detect AEs associated with any drug medication and compared the results ascertained from social media to any other data source. Study information was collated using a piloted data extraction sheet. Data were extracted on the AEs and drugs searched for and included; the methods used (such as machine learning); social media data source; volume of data analyzed; limitations of the methodology; availability of data and code; comparison data source and comparison methods; results, including the volume of AEs, and how the AEs found compared with other data sources in their seriousness, frequencies, and expectedness or novelty (new vs known knowledge); and conclusions. Results: Of the 6538 unique records screened, 73 publications representing 60 studies with a wide variety of extraction methods met our inclusion criteria. The most common social media platforms used were Twitter and online health forums. The most common comparator data source was spontaneous reporting systems, although other comparisons were also made, such as with scientific literature and product labels. Although similar patterns of AE reporting tended to be identified, the frequencies were lower in social media. Social media data were found to be useful in identifying new or unexpected AEs and in identifying AEs in a timelier manner. Conclusions: There is a large body of research comparing AEs from social media to other sources. Most studies advocate the use of social media as an adjunct to traditional data sources. Some studies also indicate the value of social media in understanding patient perspectives such as the impact of AEs, which could be better explored. International Registered Report Identifier (IRRID): RR2-10.2196/47068 UR - https://publichealth.jmir.org/2024/1/e59167 UR - http://dx.doi.org/10.2196/59167 UR - http://www.ncbi.nlm.nih.gov/pubmed/39240684 ID - info:doi/10.2196/59167 ER - TY - JOUR AU - Kongkaew, Chuenjid AU - Phan, Anh Dang Thuc AU - Janusorn, Prathan AU - Mongkhon, Pajaree PY - 2024/8/29 TI - Estimating Adverse Events Associated With Herbal Medicines Using Pharmacovigilance Databases: Systematic Review and Meta-Analysis JO - JMIR Public Health Surveill SP - e63808 VL - 10 KW - herbal medicine KW - pharmacovigilance KW - adverse event KW - spontaneous reporting system KW - meta-analysis N2 - Background: Herbal medicines (HMs) are extensively used by consumers/patients worldwide. However, their safety profiles are often poorly reported and characterized. Previous studies have documented adverse events (AEs) associated with HMs, such as hepatotoxicity, renal failure, and allergic reactions. However, the prevalence rate of AEs related to HMs has been reported to be low. To date, no systematic review and meta-analysis has comprehensively analyzed the AEs of HMs using published data acquired from pharmacovigilance (PV) databases. Objective: This study aimed to (1) estimate the reporting rate of the AEs of HMs using PV databases and (2) assess the detailed data provided in AE reports. Methods: In this systematic review and meta-analysis, MEDLINE/PubMed, SCOPUS, EMBASE, and CINAHL were systematically searched for relevant studies (until December 2023). The DerSimonian-Laird random effects model was used for pooling the data, and subgroup analyses, the meta-regression model, and sensitivity analysis were used to explore the source of heterogeneity. Crombie?s checklist was used to evaluate the risk of bias (ROB) of the included studies. Results: In total, 26 studies met the eligibility criteria. The reporting rate of the AEs of HMs ranged considerably, from 0.03% to 29.84%, with a median overall pooled estimate of 1.42% (IQR 1.12%-1.72%). Subgroup analyses combined with the meta-regression model revealed that the reporting rate of the AEs of HMs was associated with the source of the reporter (P=.01). None of the included studies provided full details of suspected herbal products, only the main ingredients were disclosed, and other potentially harmful components were not listed. Conclusions: This systematic review and meta-analysis highlighted risks related to HMs, with a wide range of reporting rates, depending on the source of the reporter. Continuous efforts are necessary to standardize consumer reporting systems in terms of the reporting form, education, and follow-up strategy to improve data quality assurance, aiming to enhance the reliability and utility of PV data for monitoring the safety of HMs. Achieving effective monitoring and reporting of these AEs necessitates collaborative efforts from diverse stakeholders, including patients/consumers, manufacturers, physicians, complementary practitioners, sellers/distributors, and health authorities. Trial Registration: PROSPERO (Prospective International Register of Systematic Reviews) CRD42021276492 UR - https://publichealth.jmir.org/2024/1/e63808 UR - http://dx.doi.org/10.2196/63808 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63808 ER - TY - JOUR AU - Erridge, Simon AU - Troup, Lucy AU - Sodergren, Hans Mikael PY - 2024/8/14 TI - Illicit Cannabis Use to Self-Treat Chronic Health Conditions in the United Kingdom: Cross-Sectional Study JO - JMIR Public Health Surveill SP - e57595 VL - 10 KW - cannabis KW - chronic pain KW - anxiety KW - multiple sclerosis KW - posttraumatic stress disorder KW - PTSD KW - fibromyalgia KW - misuse KW - cannabis misuse KW - cannabis use KW - self-treatment KW - chronic health condition KW - cross-sectional study KW - United Kingdom KW - illicit cannabis KW - adult KW - consumption KW - adults KW - survey KW - cannabis-based KW - medicinal products KW - cannabis-based medicinal products N2 - Background: In 2019, it was estimated that approximately 1.4 million adults in the United Kingdom purchased illicit cannabis to self-treat chronic physical and mental health conditions. This analysis was conducted following the rescheduling of cannabis-based medicinal products (CBMPs) in the United Kingdom but before the first specialist clinics had started treating patients. Objective: The aim of this study was to assess the prevalence of illicit cannabis consumption to treat a medically diagnosed condition following the introduction of specialist clinics that could prescribe legal CBMPs in the United Kingdom. Methods: Adults older than 18 years in the United Kingdom were invited to participate in a cross-sectional survey through YouGov between September 22 and 29, 2022. A series of questions were asked about respondents? medical diagnoses, illicit cannabis use, the cost of purchasing illicit cannabis per month, and basic demographics. The responding sample was weighted to generate a sample representative of the adult population of the United Kingdom. Modeling of population size was conducted based on an adult (18 years or older) population of 53,369,083 according to 2021 national census data. Results: There were 10,965 respondents to the questionnaire, to which weighting was applied. A total of 5700 (51.98%) respondents indicated that they were affected by a chronic health condition. The most reported condition was anxiety (n=1588, 14.48%). Of those enduring health conditions, 364 (6.38%) purchased illicit cannabis to self-treat health conditions. Based on survey responses, it was modeled that 1,770,627 (95% CI 1,073,791?2,467,001) individuals consume illicit cannabis for health conditions across the United Kingdom. In the multivariable logistic regression, the following were associated with an increased likelihood of reporting illicit cannabis use for health reasons?chronic pain, fibromyalgia, posttraumatic stress disorder, multiple sclerosis, other mental health disorders, male sex, younger age, living in London, being unemployed or not working for other reasons, and working part-time (P<.05). Conclusions: This study highlights the scale of illicit cannabis use for health reasons in the United Kingdom and the potential barriers to accessing legally prescribed CBMPs. This is an important step in developing harm reduction policies to transition these individuals, where appropriate, to CBMPs. Such policies are particularly important considering the potential risks from harmful contaminants of illicit cannabis and self-treating a medical condition without clinical oversight. Moreover, it emphasizes the need for further funding of randomized controlled trials and the use of novel methodologies to determine the efficacy of CBMPs and their use in common chronic conditions. UR - https://publichealth.jmir.org/2024/1/e57595 UR - http://dx.doi.org/10.2196/57595 ID - info:doi/10.2196/57595 ER - TY - JOUR AU - Postma, J. Doerine AU - Heijkoop, A. Magali L. AU - De Smet, M. Peter A. G. AU - Notenboom, Kim AU - Leufkens, M. Hubert G. AU - Mantel-Teeuwisse, K. Aukje PY - 2024/8/6 TI - Identifying Medicine Shortages With the Twitter Social Network: Retrospective Observational Study JO - J Med Internet Res SP - e51317 VL - 26 KW - medicine shortages KW - signal detection KW - social media KW - Twitter social network KW - drug shortage KW - Twitter N2 - Background: Early identification is critical for mitigating the impact of medicine shortages on patients. The internet, specifically social media, is an emerging source of health data. Objective: This study aimed to explore whether a routine analysis of data from the Twitter social network can detect signals of a medicine shortage and serve as an early warning system and, if so, for which medicines or patient groups. Methods: Medicine shortages between January 31 and December 1, 2019, were collected from the Dutch pharmacists? society?s national catalog Royal Dutch Pharmacists Association (KNMP) Farmanco. Posts on these shortages were collected by searching for the name, the active pharmaceutical ingredient, or the first word of the brand name of the medicines in shortage. Posts were then selected based on relevant keywords that potentially indicated a shortage and the percentage of shortages with at least 1 post was calculated. The first posts per shortage were analyzed for their timing (median number of days, including the IQR) versus the national catalog, also stratified by disease and medicine characteristics. The content of the first post per shortage was analyzed descriptively for its reporting stakeholder and the nature of the post. Results: Of the 341 medicine shortages, 102 (29.9%) were mentioned on Twitter. Of these 102 shortages, 18 (5.3% of the total) were mentioned prior to or simultaneous to publication by KNMP Farmanco. Only 4 (1.2%) of these were mentioned on Twitter more than 14 days before. On average, posts were published with a median delay of 37 (IQR 7-81) days to publication by KNMP Farmanco. Shortages mentioned on Twitter affected a greater number of patients and lasted longer than those that were not mentioned. We could not conclusively relate either the presence or absence on Twitter to a disease area or route of administration of the medicine in shortage. The first posts on the 102 shortages were mainly published by patients (n=51, 50.0%) and health care professionals (n=46, 45.1%). We identified 8 categories of nature of content. Sharing personal experience (n=44, 43.1%) was the most common category. Conclusions: The Twitter social network is not a suitable early warning system for medicine shortages. Twitter primarily echoes already-known information rather than spreads new information. However, Twitter or potentially any other social media platform provides the opportunity for future qualitative research in the increasingly important field of medicine shortages that investigates how a larger population of patients is affected by shortages. UR - https://www.jmir.org/2024/1/e51317 UR - http://dx.doi.org/10.2196/51317 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51317 ER - TY - JOUR AU - Kizaki, Hayato AU - Satoh, Hiroki AU - Ebara, Sayaka AU - Watabe, Satoshi AU - Sawada, Yasufumi AU - Imai, Shungo AU - Hori, Satoko PY - 2024/7/23 TI - Construction of a Multi-Label Classifier for Extracting Multiple Incident Factors From Medication Incident Reports in Residential Care Facilities: Natural Language Processing Approach JO - JMIR Med Inform SP - e58141 VL - 12 KW - residential facilities KW - incidents KW - non-medical staff KW - natural language processing KW - risk management N2 - Background: Medication safety in residential care facilities is a critical concern, particularly when nonmedical staff provide medication assistance. The complex nature of medication-related incidents in these settings, coupled with the psychological impact on health care providers, underscores the need for effective incident analysis and preventive strategies. A thorough understanding of the root causes, typically through incident-report analysis, is essential for mitigating medication-related incidents. Objective: We aimed to develop and evaluate a multilabel classifier using natural language processing to identify factors contributing to medication-related incidents using incident report descriptions from residential care facilities, with a focus on incidents involving nonmedical staff. Methods: We analyzed 2143 incident reports, comprising 7121 sentences, from residential care facilities in Japan between April 1, 2015, and March 31, 2016. The incident factors were annotated using sentences based on an established organizational factor model and previous research findings. The following 9 factors were defined: procedure adherence, medicine, resident, resident family, nonmedical staff, medical staff, team, environment, and organizational management. To assess the label criteria, 2 researchers with relevant medical knowledge annotated a subset of 50 reports; the interannotator agreement was measured using Cohen ?. The entire data set was subsequently annotated by 1 researcher. Multiple labels were assigned to each sentence. A multilabel classifier was developed using deep learning models, including 2 Bidirectional Encoder Representations From Transformers (BERT)?type models (Tohoku-BERT and a University of Tokyo Hospital BERT pretrained with Japanese clinical text: UTH-BERT) and an Efficiently Learning Encoder That Classifies Token Replacements Accurately (ELECTRA), pretrained on Japanese text. Both sentence- and report-level training were performed; the performance was evaluated by the F1-score and exact match accuracy through 5-fold cross-validation. Results: Among all 7121 sentences, 1167, 694, 2455, 23, 1905, 46, 195, 1104, and 195 included ?procedure adherence,? ?medicine,? ?resident,? ?resident family,? ?nonmedical staff,? ?medical staff,? ?team,? ?environment,? and ?organizational management,? respectively. Owing to limited labels, ?resident family? and ?medical staff? were omitted from the model development process. The interannotator agreement values were higher than 0.6 for each label. A total of 10, 278, and 1855 reports contained no, 1, and multiple labels, respectively. The models trained using the report data outperformed those trained using sentences, with macro F1-scores of 0.744, 0.675, and 0.735 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. The report-trained models also demonstrated better exact match accuracy, with 0.411, 0.389, and 0.399 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. Notably, the accuracy was consistent even when the analysis was confined to reports containing multiple labels. Conclusions: The multilabel classifier developed in our study demonstrated potential for identifying various factors associated with medication-related incidents using incident reports from residential care facilities. Thus, this classifier can facilitate prompt analysis of incident factors, thereby contributing to risk management and the development of preventive strategies. UR - https://medinform.jmir.org/2024/1/e58141 UR - http://dx.doi.org/10.2196/58141 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58141 ER - TY - JOUR AU - Holdefer, A. Ashley AU - Pizarro, Jeno AU - Saunders-Hastings, Patrick AU - Beers, Jeffrey AU - Sang, Arianna AU - Hettinger, Zachary Aaron AU - Blumenthal, Joseph AU - Martinez, Erik AU - Jones, Daniel Lance AU - Deady, Matthew AU - Ezzeldin, Hussein AU - Anderson, A. Steven PY - 2024/7/15 TI - Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study JO - JMIR Public Health Surveill SP - e49811 VL - 10 KW - adverse event KW - vaccine safety KW - computable phenotype KW - postmarket surveillance system KW - real-world data KW - validation study KW - Food and Drug Administration KW - electronic health records KW - COVID-19 vaccine N2 - Background: Adverse events associated with vaccination have been evaluated by epidemiological studies and more recently have gained additional attention with the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19. Objective: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the Food and Drug Administration?s postmarket surveillance capabilities while minimizing public burden. This study aimed to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify 5 AESIs being monitored by the Center for Disease Control and Prevention for COVID-19 or other vaccines: anaphylaxis, Guillain-Barré syndrome, myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome, and febrile seizure. This study examined whether these phenotypes have sufficiently high positive predictive value (PPV) to ensure that the cases selected for surveillance are reasonably likely to be a postbiologic adverse event. This allows patient privacy, and security concerns for the data sharing of patients who had nonadverse events can be properly accounted for when evaluating the cost-benefit aspect of our approach. Methods: AESI phenotype algorithms were developed to apply to electronic health record data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. To validate the performance of the algorithms, we applied them to electronic health record data from a US academic health system and provided a sample of cases for clinicians to evaluate. Performance was assessed using PPV. Results: With a PPV of 93.3%, our anaphylaxis algorithm performed the best. The PPVs for our febrile seizure, myocarditis/pericarditis, thrombocytopenia syndrome, and Guillain-Barré syndrome algorithms were 89%, 83.5%, 70.2%, and 47.2%, respectively. Conclusions: Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI postmarket detection. UR - https://publichealth.jmir.org/2024/1/e49811 UR - http://dx.doi.org/10.2196/49811 UR - http://www.ncbi.nlm.nih.gov/pubmed/39008361 ID - info:doi/10.2196/49811 ER - TY - JOUR AU - Li, Yiming AU - Li, Jianfu AU - Dang, Yifang AU - Chen, Yong AU - Tao, Cui PY - 2024/7/15 TI - Adverse Events of COVID-19 Vaccines in the United States: Temporal and Spatial Analysis JO - JMIR Public Health Surveill SP - e51007 VL - 10 KW - COVID-19 KW - vaccine KW - COVID-19 vaccine KW - adverse drug event KW - ADE KW - Vaccine Adverse Event Reporting System KW - VAERS KW - adverse event following immunization KW - AEFI N2 - Background: The COVID-19 pandemic, caused by SARS-CoV-2, has had a profound impact worldwide, leading to widespread morbidity and mortality. Vaccination against COVID-19 is a critical tool in controlling the spread of the virus and reducing the severity of the disease. However, the rapid development and deployment of COVID-19 vaccines have raised concerns about potential adverse events following immunization (AEFIs). Understanding the temporal and spatial patterns of these AEFIs is crucial for an effective public health response and vaccine safety monitoring. Objective: This study aimed to analyze the temporal and spatial characteristics of AEFIs associated with COVID-19 vaccines in the United States reported to the Vaccine Adverse Event Reporting System (VAERS), thereby providing insights into the patterns and distributions of the AEFIs, the safety profile of COVID-19 vaccines, and potential risk factors associated with the AEFIs. Methods: We conducted a retrospective analysis of administration data from the Centers for Disease Control and Prevention (n=663,822,575) and reports from the surveillance system VAERS (n=900,522) between 2020 and 2022. To gain a broader understanding of postvaccination AEFIs reported, we categorized them into system organ classes (SOCs) according to the Medical Dictionary for Regulatory Activities. Additionally, we performed temporal analysis to examine the trends of AEFIs in all VAERS reports, those related to Pfizer-BioNTech and Moderna, and the top 10 AEFI trends in serious reports. We also compared the similarity of symptoms across various regions within the United States. Results: Our findings revealed that the most frequently reported symptoms following COVID-19 vaccination were headache (n=141,186, 15.68%), pyrexia (n=122,120, 13.56%), and fatigue (n=121,910, 13.54%). The most common symptom combination was chills and pyrexia (n=56,954, 6.32%). Initially, general disorders and administration site conditions (SOC 22) were the most prevalent class reported. Moderna exhibited a higher reporting rate of AEFIs compared to Pfizer-BioNTech. Over time, we observed a decreasing reporting rate of AEFIs associated with COVID-19 vaccines. In addition, the overall rates of AEFIs between the Pfizer-BioNTech and Moderna vaccines were comparable. In terms of spatial analysis, the middle and north regions of the United States displayed a higher reporting rate of AEFIs associated with COVID-19 vaccines, while the southeast and south-central regions showed notable similarity in symptoms reported. Conclusions: This study provides valuable insights into the temporal and spatial patterns of AEFIs associated with COVID-19 vaccines in the United States. The findings underscore the critical need for increasing vaccination coverage, as well as ongoing surveillance and monitoring of AEFIs. Implementing targeted monitoring programs can facilitate the effective and efficient management of AEFIs, enhancing public confidence in future COVID-19 vaccine campaigns. UR - https://publichealth.jmir.org/2024/1/e51007 UR - http://dx.doi.org/10.2196/51007 UR - http://www.ncbi.nlm.nih.gov/pubmed/39008362 ID - info:doi/10.2196/51007 ER - TY - JOUR AU - Siefried, J. Krista AU - Bascombe, Florence AU - Clifford, Brendan AU - Liu, Zhixin AU - Middleton, Peter AU - Kay-Lambkin, Frances AU - Freestone, Jack AU - Herman, Daniel AU - Millard, Michael AU - Steele, Maureen AU - Acheson, Liam AU - Moller, Carl AU - Bath, Nicky AU - Ezard, Nadine PY - 2024/7/3 TI - Effect of a Smartphone App (S-Check) on Actual and Intended Help-Seeking and Motivation to Change Methamphetamine Use Among Adult Consumers of Methamphetamine in Australia: Randomized Waitlist-Controlled Trial JO - JMIR Mhealth Uhealth SP - e55663 VL - 12 KW - methamphetamine KW - smartphone app KW - behavior change KW - help-seeking KW - motivation to change KW - mHealth KW - mobile health KW - app KW - apps KW - application KW - applications KW - smartphone KW - smartphones KW - motivation KW - motivational KW - RCT KW - randomized KW - controlled trial KW - controlled trials KW - drug KW - drugs KW - substance use KW - engagement KW - substance abuse KW - mobile phone N2 - Background: Interventions are required that address delays in treatment-seeking and low treatment coverage among people consuming methamphetamine. Objective: We aim to determine whether a self-administered smartphone-based intervention, the ?S-Check app? can increase help-seeking and motivation to change methamphetamine use, and determine factors associated with app engagement. Methods: This study is a randomized, 28-day waitlist-controlled trial. Consenting adults residing in Australia who reported using methamphetamine at least once in the last month were eligible to download the app for free from Android or iOS app stores. Those randomized to the intervention group had immediate access to the S-Check app, the control group was wait-listed for 28 days before gaining access, and then all had access until day 56. Actual help-seeking and intention to seek help were assessed by the modified Actual Help Seeking Questionnaire (mAHSQ), modified General Help Seeking Questionnaire, and motivation to change methamphetamine use by the modified readiness ruler. ?2 comparisons of the proportion of positive responses to the mAHSQ, modified General Help Seeking Questionnaire, and modified readiness ruler were conducted between the 2 groups. Logistic regression models compared the odds of actual help-seeking, intention to seek help, and motivation to change at day 28 between the 2 groups. Secondary outcomes were the most commonly accessed features of the app, methamphetamine use, feasibility and acceptability of the app, and associations between S-Check app engagement and participant demographic and methamphetamine use characteristics. Results: In total, 560 participants downloaded the app; 259 (46.3%) completed eConsent and baseline; and 84 (32.4%) provided data on day 28. Participants in the immediate access group were more likely to seek professional help (mAHSQ) at day 28 than those in the control group (n=15, 45.5% vs n=12, 23.5%; ?21=4.42, P=.04). There was no significant difference in the odds of actual help-seeking, intention to seek help, or motivation to change methamphetamine use between the 2 groups on the primary logistic regression analyses, while in the ancillary analyses, the imputed data set showed a significant difference in the odds of seeking professional help between participants in the immediate access group compared to the waitlist control group (adjusted odds ratio 2.64, 95% CI 1.19-5.83, P=.02). For participants not seeking help at baseline, each minute in the app increased the likelihood of seeking professional help by day 28 by 8% (ratio 1.08, 95% CI 1.02-1.22, P=.04). Among the intervention group, a 10-minute increase in app engagement time was associated with a decrease in days of methamphetamine use by 0.4 days (regression coefficient [?] ?0.04, P=.02). Conclusions: The S-Check app is a feasible low-resource self-administered intervention for adults in Australia who consume methamphetamine. Study attrition was high and, while common in mobile health interventions, warrants larger studies of the S-Check app. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12619000534189; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377288&isReview=true UR - https://mhealth.jmir.org/2024/1/e55663 UR - http://dx.doi.org/10.2196/55663 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55663 ER - TY - JOUR AU - van Wyk, S. Susanna AU - Nliwasa, Marriott AU - Lu, Fang-Wen AU - Lan, Chih-Chan AU - Seddon, A. James AU - Hoddinott, Graeme AU - Viljoen, Lario AU - Günther, Gunar AU - Ruswa, Nunurai AU - Shah, Sarita N. AU - Claassens, Mareli PY - 2024/6/26 TI - Drug-Resistant Tuberculosis Case-Finding Strategies: Scoping Review JO - JMIR Public Health Surveill SP - e46137 VL - 10 KW - tuberculosis KW - drug-resistant tuberculosis KW - drug-resistant tuberculosis case finding KW - drug-resistant tuberculosis case detection KW - drug-resistant tuberculosis screening KW - drug-resistant tuberculosis contact investigation KW - scoping review KW - TB symptom KW - anti-tuberculosis drug KW - strategies KW - multidrug-resistant KW - systematic review KW - drug resistant KW - drug resistance KW - medication KW - diagnosis KW - screening N2 - Background: Finding individuals with drug-resistant tuberculosis (DR-TB) is important to control the pandemic and improve patient clinical outcomes. To our knowledge, systematic reviews assessing the effectiveness, cost-effectiveness, acceptability, and feasibility of different DR-TB case-finding strategies to inform research, policy, and practice, have not been conducted and the scope of primary research is unknown. Objective: We therefore assessed the available literature on DR-TB case-finding strategies. Methods: We looked at systematic reviews, trials, qualitative studies, diagnostic test accuracy studies, and other primary research that sought to improve DR-TB case detection specifically. We excluded studies that included patients seeking care for tuberculosis (TB) symptoms, patients already diagnosed with TB, or were laboratory-based. We searched the academic databases of MEDLINE, Embase, The Cochrane Library, Africa-Wide Information, CINAHL (Cumulated Index to Nursing and Allied Health Literature), Epistemonikos, and PROSPERO (The International Prospective Register of Systematic Reviews) using no language or date restrictions. We screened titles, abstracts, and full-text articles in duplicate. Data extraction and analyses were carried out in Excel (Microsoft Corp). Results: We screened 3646 titles and abstracts and 236 full-text articles. We identified 6 systematic reviews and 61 primary studies. Five reviews described the yield of contact investigation and focused on household contacts, airline contacts, comparison between drug-susceptible tuberculosis and DR-TB contacts, and concordance of DR-TB profiles between index cases and contacts. One review compared universal versus selective drug resistance testing. Primary studies described (1) 34 contact investigations, (2) 17 outbreak investigations, (3) 3 airline contact investigations, (4) 5 epidemiological analyses, (5) 1 public-private partnership program, and (6) an e-registry program. Primary studies were all descriptive and included cross-sectional and retrospective reviews of program data. No trials were identified. Data extraction from contact investigations was difficult due to incomplete reporting of relevant information. Conclusions: Existing descriptive reviews can be updated, but there is a dearth of knowledge on the effectiveness, cost-effectiveness, acceptability, and feasibility of DR-TB case-finding strategies to inform policy and practice. There is also a need for standardization of terminology, design, and reporting of DR-TB case-finding studies. UR - https://publichealth.jmir.org/2024/1/e46137 UR - http://dx.doi.org/10.2196/46137 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/46137 ER - TY - JOUR AU - Xu, Stanley AU - Sy, S. Lina AU - Hong, Vennis AU - Holmquist, J. Kimberly AU - Qian, Lei AU - Farrington, Paddy AU - Bruxvoort, J. Katia AU - Klein, P. Nicola AU - Fireman, Bruce AU - Han, Bing AU - Lewin, J. Bruno PY - 2024/6/25 TI - Ischemic Stroke After Bivalent COVID-19 Vaccination: Self-Controlled Case Series Study JO - JMIR Public Health Surveill SP - e53807 VL - 10 KW - ischemic stroke KW - bivalent COVID-19 vaccine KW - influenza vaccine KW - self-controlled case series KW - coadministration KW - ischemic KW - stroke KW - TIA KW - transient ischemic attack KW - ischemia KW - cardiovascular KW - COVID-19 KW - SARS-CoV-2 KW - vaccine KW - vaccines KW - vaccination KW - association KW - correlation KW - risk KW - risks KW - adverse KW - side effect KW - subgroup analyses KW - subgroup analysis KW - bivalent KW - influenza KW - infectious KW - respiratory KW - incidence KW - case series N2 - Background: The potential association between bivalent COVID-19 vaccination and ischemic stroke remains uncertain, despite several studies conducted thus far. Objective: This study aimed to evaluate the risk of ischemic stroke following bivalent COVID-19 vaccination during the 2022-2023 season. Methods: A self-controlled case series study was conducted among members aged 12 years and older who experienced ischemic stroke between September 1, 2022, and March 31, 2023, in a large health care system. Ischemic strokes were identified using International Classification of Diseases, Tenth Revision codes in emergency departments and inpatient settings. Exposures were Pfizer-BioNTech or Moderna bivalent COVID-19 vaccination. Risk intervals were prespecified as 1-21 days and 1-42 days after bivalent vaccination; all non?risk-interval person-time served as the control interval. The incidence of ischemic stroke was compared in the risk interval and control interval using conditional Poisson regression. We conducted overall and subgroup analyses by age, history of SARS-CoV-2 infection, and coadministration of influenza vaccine. When an elevated risk was detected, we performed a chart review of ischemic strokes and analyzed the risk of chart-confirmed ischemic stroke. Results: With 4933 ischemic stroke events, we found no increased risk within the 21-day risk interval for the 2 vaccines and by subgroups. However, risk of ischemic stroke was elevated within the 42-day risk interval among individuals aged younger than 65 years with coadministration of Pfizer-BioNTech bivalent and influenza vaccines on the same day; the relative incidence (RI) was 2.13 (95% CI 1.01-4.46). Among those who also had a history of SARS-CoV-2 infection, the RI was 3.94 (95% CI 1.10-14.16). After chart review, the RIs were 2.34 (95% CI 0.97-5.65) and 4.27 (95% CI 0.97-18.85), respectively. Among individuals aged younger than 65 years who received Moderna bivalent vaccine and had a history of SARS-CoV-2 infection, the RI was 2.62 (95% CI 1.13-6.03) before chart review and 2.24 (95% CI 0.78-6.47) after chart review. Stratified analyses by sex did not show a significantly increased risk of ischemic stroke after bivalent vaccination. Conclusions: While the point estimate for the risk of chart-confirmed ischemic stroke was elevated in a risk interval of 1-42 days among individuals younger than 65 years with coadministration of Pfizer-BioNTech bivalent and influenza vaccines on the same day and among individuals younger than 65 years who received Moderna bivalent vaccine and had a history of SARS-CoV-2 infection, the risk was not statistically significant. The potential association between bivalent vaccination and ischemic stroke in the 1-42?day analysis warrants further investigation among individuals younger than 65 years with influenza vaccine coadministration and prior SARS-CoV-2 infection. Furthermore, the findings on ischemic stroke risk after bivalent COVID-19 vaccination underscore the need to evaluate monovalent COVID-19 vaccine safety during the 2023-2024 season. UR - https://publichealth.jmir.org/2024/1/e53807 UR - http://dx.doi.org/10.2196/53807 UR - http://www.ncbi.nlm.nih.gov/pubmed/38916940 ID - info:doi/10.2196/53807 ER - TY - JOUR AU - Karapetiantz, Pierre AU - Audeh, Bissan AU - Redjdal, Akram AU - Tiffet, Théophile AU - Bousquet, Cédric AU - Jaulent, Marie-Christine PY - 2024/6/18 TI - Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study JO - J Med Internet Res SP - e46176 VL - 26 KW - pharmacovigilance KW - social media KW - scraper KW - natural language processing KW - signal detection KW - graphical user interface N2 - Background: To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media?s potential remains largely untapped in real-world scenarios. Objective: The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. Methods: To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums? posts extraction, (2) web forums? posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. Results: Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. Conclusions: We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events. UR - https://www.jmir.org/2024/1/e46176 UR - http://dx.doi.org/10.2196/46176 UR - http://www.ncbi.nlm.nih.gov/pubmed/38888956 ID - info:doi/10.2196/46176 ER - TY - JOUR AU - Siste, Kristiana AU - Ophinni, Youdiil AU - Hanafi, Enjeline AU - Yamada, Chika AU - Novalino, Reza AU - Limawan, P. Albert AU - Beatrice, Evania AU - Rafelia, Vania AU - Alison, Peter AU - Matsumoto, Toshihiko AU - Sakamoto, Ryota PY - 2024/6/18 TI - Relapse Prevention Group Therapy in Indonesia Involving Peers via Videoconferencing for Substance Use Disorder: Development and Feasibility Study JO - JMIR Form Res SP - e50452 VL - 8 KW - substance use disorder KW - cognitive behavioral therapy KW - telemedicine KW - peer involvement KW - Indonesia KW - substance use disorders KW - digital intervention KW - COVID-19 KW - psychotherapy KW - drug KW - mobile phone N2 - Background: Substance use disorder (SUD) is a major health issue in Indonesia, where several barriers to treatment exist, including inaccessibility to treatment services, stigma, and criminalization of drug issues. Peer involvement and the use of telemedicine to deliver psychotherapy are promising approaches to overcome these barriers. Objective: This study aims (1) to describe the development of a new group psychotherapy coprovided by a health care worker and a peer and (2) to evaluate the acceptability, practicality, and preliminary outcomes of the program delivered via videoconferencing in Indonesia. Methods: Building upon an established relapse prevention therapy in Japan, we developed a 3-month weekly group therapy module in the Indonesian language. Adjustments were made via focus group discussions with local stakeholders in terms of substance types, understandability, inclusive language, and cultural relevance. A pilot study was conducted to test the new module provided by a peer and a psychiatrist via videoconferencing, termed tele-Indonesia Drug Addiction Relapse Prevention Program (tele-Indo-DARPP), with a pre- and postcontrolled design. We analyzed data from semistructured feedback interviews and outcome measurements, including the number of days using substances and quality of life, and compared the intervention (tele-Indo-DARPP added to treatment as usual [TAU]) and control (TAU only) arms. Results: In total, 8 people diagnosed with SUD participated in the pilot study with a mean age of 37 (SD 12.8) years. All were men, and 7 (88%) used sedatives as the primary substance. Collectively, they attended 44 of the 48 tele-Indo-DARPP sessions. A total of 3 out of 4 (75%) preferred telemedicine rather than in-person therapy. Positive acceptability and practicality were shown from qualitative feedback, in which the participants who joined the tele-Indo-DARPP reported that they liked the convenience of joining from home and that they were able to open up about personal matters, received helpful advice from peers, and received support from other participants. Providers reported that they feel the module was provider-friendly, and the session was convenient to join without diminishing rapport-building. Meanwhile, troubles with the internet connection and difficulty in comprehending some terminology in the workbook were reported. The intervention arm showed better improvements in psychological health and anxiety symptoms. Conclusions: Group psychotherapy via videoconferencing coprovided by health care workers and peers was acceptable and practical for participants with SUD and service providers in this study. A large-scale study is warranted to examine the effectiveness of the newly developed module in Indonesia. UR - https://formative.jmir.org/2024/1/e50452 UR - http://dx.doi.org/10.2196/50452 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/50452 ER - TY - JOUR AU - Zhao, Li AU - Guo, Yiping AU - Zhou, Xuelei AU - Mao, Wei AU - Chen, Linlin AU - Xie, Ying AU - Li, Linji PY - 2024/6/12 TI - Efficacy and Safety of Remimazolam Versus Etomidate for Induction of General Anesthesia: Protocol for a Systematic Review and Meta-Analysis JO - JMIR Res Protoc SP - e55948 VL - 13 KW - general anesthesia KW - anesthesia induction KW - postinduction hypotension KW - remimazolam KW - etomidate KW - meta-analysis N2 - Background: Postinduction hypotension (PIHO) is a hemodynamic abnormality commonly observed during the induction of general anesthesia. Etomidate is considered a safer drug for the induction of anesthesia because it has only minor adverse effects on the cardiovascular and pulmonary systems. Recent evidence indicates that the novel benzodiazepine remimazolam has minimal inhibitory effects on the circulation and respiration. However, the efficacy and safety of remimazolam versus etomidate in the induction of anesthesia are unclear. Objective: To further understand the potential of remimazolam in anesthesia induction, it is necessary to design a meta-analysis to compare its effects versus the classic safe anesthetic etomidate. The aim of this study is to determine which drug has more stable hemodynamics and a lower incidence of PIHO. Our study will also yield data on sedation efficiency, time to loss of consciousness, time to awakening, incidence of injection pain, and postoperative nausea and vomiting with the two drugs. Methods: We plan to search the Web of Science, Cochrane Library, Embase, PubMed, China National Knowledge Infrastructure, and Wanfang databases from the date of their creation until March 31, 2025. The language is limited to English and Chinese. The search terms are ?randomized controlled trials,? ?etomidate,? and ?remimazolam.? The incidence of PIHO is the primary outcome measure. Secondary outcomes include depth of anesthesia after induction, sedation success rate, time to loss of consciousness, hemodynamic profiles, recovery time, incidence of injection pain, and postoperative nausea and vomiting. Reviews, meta-analyses, case studies, abstracts from conferences, and commentaries will not be included. The heterogeneity of the results will be evaluated by sensitivity and subgroup analyses. RevMan software and Stata software will be used for data analysis. We will evaluate the quality of included studies using version 2 of the Cochrane risk-of-bias tool. The confidence of the evidence will be assessed through the Grading of Recommendations, Assessments, Developments, and Evaluations system. Results: The protocol was registered in the international PROSPERO (Prospective Register of Systematic Reviews) registry in November 2023. As of June 2024, we have performed a preliminary article search and retrieval for further review. The review and analyses are expected to be completed in March 2025. We expect to submit manuscripts for peer review by the end of June 2025. Conclusions: By synthesizing the available evidence and comparing remimazolam and etomidate, we hope to provide valuable insights into the selection of anesthesia-inducing drugs to reduce the incidence of PIHO and improve patient prognosis. Trial Registration: PROSPERO CRD42023463120; https://tinyurl.com/333jb8bm International Registered Report Identifier (IRRID): PRR1-10.2196/55948 UR - https://www.researchprotocols.org/2024/1/e55948 UR - http://dx.doi.org/10.2196/55948 UR - http://www.ncbi.nlm.nih.gov/pubmed/38865185 ID - info:doi/10.2196/55948 ER - TY - JOUR AU - Salvi, Amey AU - Gillenwater, A. Logan AU - Cockrum, P. Brandon AU - Wiehe, E. Sarah AU - Christian, Kaitlyn AU - Cayton, John AU - Bailey, Timothy AU - Schwartz, Katherine AU - Dir, L. Allyson AU - Ray, Bradley AU - Aalsma, C. Matthew AU - Reda, Khairi PY - 2024/6/11 TI - Development of a Real-Time Dashboard for Overdose Touchpoints: User-Centered Design Approach JO - JMIR Hum Factors SP - e57239 VL - 11 KW - overdose prevention KW - dashboards KW - fatality review KW - data integration KW - visualizations KW - visualization KW - dashboard KW - fatality KW - death KW - overdose KW - overdoses KW - overdosing KW - prevention KW - develop KW - development KW - design KW - interview KW - interviews KW - focus group KW - focus groups KW - touchpoints KW - touchpoint KW - substance abuse KW - drug abuse N2 - Background: Overdose Fatality Review (OFR) is an important public health tool for shaping overdose prevention strategies in communities. However, OFR teams review only a few cases at a time, which typically represent a small fraction of the total fatalities in their jurisdiction. Such limited review could result in a partial understanding of local overdose patterns, leading to policy recommendations that do not fully address the broader community needs. Objective: This study explored the potential to enhance conventional OFRs with a data dashboard, incorporating visualizations of touchpoints?events that precede overdoses?to highlight prevention opportunities. Methods: We conducted 2 focus groups and a survey of OFR experts to characterize their information needs and design a real-time dashboard that tracks and measures decedents? past interactions with services in Indiana. Experts (N=27) were engaged, yielding insights on essential data features to incorporate and providing feedback to guide the development of visualizations. Results: The findings highlighted the importance of showing decedents? interactions with health services (emergency medical services) and the justice system (incarcerations). Emphasis was also placed on maintaining decedent anonymity, particularly in small communities, and the need for training OFR members in data interpretation. The developed dashboard summarizes key touchpoint metrics, including prevalence, interaction frequency, and time intervals between touchpoints and overdoses, with data viewable at the county and state levels. In an initial evaluation, the dashboard was well received for its comprehensive data coverage and its potential for enhancing OFR recommendations and case selection. Conclusions: The Indiana touchpoints dashboard is the first to display real-time visualizations that link administrative and overdose mortality data across the state. This resource equips local health officials and OFRs with timely, quantitative, and spatiotemporal insights into overdose risk factors in their communities, facilitating data-driven interventions and policy changes. However, fully integrating the dashboard into OFR practices will likely require training teams in data interpretation and decision-making. UR - https://humanfactors.jmir.org/2024/1/e57239 UR - http://dx.doi.org/10.2196/57239 UR - http://www.ncbi.nlm.nih.gov/pubmed/38861717 ID - info:doi/10.2196/57239 ER - TY - JOUR AU - Priyadarshini, Rekha AU - Eerike, Madhavi AU - Varatharajan, Sakthivadivel AU - Ramaswamy, Gomathi AU - Raj, Marshall Gerard AU - Cherian, Jose Jerin AU - Rajendran, Priyadharsini AU - Gunasekaran, Venugopalan AU - Rao, V. Shailaja AU - Konda, Rao Venu Gopala PY - 2024/6/11 TI - Assessing the Efficacy of the ARMOR Tool?Based Deprescribing Intervention for Fall Risk Reduction in Older Patients Taking Fall Risk?Increasing Drugs (DeFRID Trial): Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e55638 VL - 13 KW - deprescribing KW - geriatric KW - fall risk?increasing drugs KW - FRIDs KW - ARMOR tool KW - Assess, Review, Minimize, Optimize, and Reassess KW - falls KW - older patients KW - fall risk N2 - Background: Falls in older patients can lead to serious health complications and increased health care costs. Fall risk?increasing drugs (FRIDs) are a group of drugs that may induce falls or increase the tendency to fall (ie, fall risk). Deprescribing is the process of withdrawal from an inappropriate medication, supervised by a health care professional, with the goal of managing polypharmacy and improving outcomes. Objective: This study aims to assess the effectiveness of a deprescribing intervention based on the Assess, Review, Minimize, Optimize, and Reassess (ARMOR) tool in reducing the risk of falls in older patients and evaluate the cost-effectiveness of deprescribing FRIDs. Methods: This is an open-label, parallel-group randomized controlled academic trial. Individuals aged 60-80 years who are currently taking 5 or more prescribed drugs, including at least 1 FRID, will be recruited. Demographic data, medical conditions, medication lists, orthostatic hypotension, and fall history details will be collected. Fall concern will be assessed using the Fall Efficacy Scale, and fall risk will be assessed by the Timed Up and Go test and Tinetti Performance-Oriented Mobility Assessment tool. In this study, all treating physicians will be randomized using a stratified randomization method based on seniority. Randomized physicians will do deprescribing with the ARMOR tool for patients on FRIDs. Participants will maintain diaries, and monthly phone follow-ups will be undertaken to monitor falls and adverse events. Physical assessments will be performed to evaluate fall risk every 3 months for a year. The rationality of prescription drugs will be evaluated using the World Health Organization?s core indicators. Results: The study received a grant from the Indian Council of Medical Research?Safe and Rational Use of Medicine in October 2023. The study is scheduled to commence in April 2024 and conclude by 2026. Efficacy will be measured by fall frequency and changes in fall risk scores. Cost-effectiveness analysis will also include the incremental cost-effectiveness ratio calculation. Adverse events related to deprescription will be recorded. Conclusions: This trial will provide essential insights into the efficacy of the ARMOR tool in reducing falls among the geriatric population who are taking FRIDs. Additionally, it will provide valuable information on the cost-effectiveness of deprescribing practices, offering significant implications for improving the well-being of older patients and optimizing health care resource allocation. The findings from this study will be pertinent for health care professionals, policy makers, and researchers focused on geriatric care and fall prevention strategies. Trial Registration: Clinical Trials Registry ? India CTRI/2023/12/060516; https://ctri.nic.in/Clinicaltrials/pubview2.php International Registered Report Identifier (IRRID): PRR1-10.2196/55638 UR - https://www.researchprotocols.org/2024/1/e55638 UR - http://dx.doi.org/10.2196/55638 UR - http://www.ncbi.nlm.nih.gov/pubmed/38861709 ID - info:doi/10.2196/55638 ER - TY - JOUR AU - Ball, Robert AU - Talal, H. Andrew AU - Dang, Oanh AU - Muñoz, Monica AU - Markatou, Marianthi PY - 2024/6/6 TI - Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration JO - J Med Internet Res SP - e50274 VL - 26 KW - drug safety KW - artificial intelligence KW - machine learning KW - natural language processing KW - causal inference KW - case-based reasoning KW - clinical decision support UR - https://www.jmir.org/2024/1/e50274 UR - http://dx.doi.org/10.2196/50274 UR - http://www.ncbi.nlm.nih.gov/pubmed/38842929 ID - info:doi/10.2196/50274 ER - TY - JOUR AU - Hopcroft, EM Lisa AU - Curtis, J. Helen AU - Croker, Richard AU - Pretis, Felix AU - Inglesby, Peter AU - Evans, David AU - Bacon, Sebastian AU - Goldacre, Ben AU - Walker, J. Alex AU - MacKenna, Brian PY - 2024/6/5 TI - Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study JO - JMIR Public Health Surveill SP - e51323 VL - 10 KW - electronic health records KW - primary care KW - general practice KW - opioid analgesics KW - data science KW - implementation science KW - data-driven KW - identification KW - intervention KW - implementations KW - proof of concept KW - opioid KW - unbiased KW - prescribing data KW - analysis tool N2 - Background: We have previously demonstrated that opioid prescribing increased by 127% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies, and there is some evidence that progress is being made. Objective: We sought to extend our previous work and develop a data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented. Methods: We analyzed 5 years of prescribing data (December 2014 to November 2019) for 3 opioid prescribing measures?total opioid prescribing as oral morphine equivalent per 1000 registered population, the number of high-dose opioids prescribed per 1000 registered population, and the number of high-dose opioids as a percentage of total opioids prescribed. Using a data-driven approach, we applied a modified version of our change detection Python library to identify reductions in these measures over time, which may be consistent with the successful implementation of an intervention to reduce opioid prescribing. This analysis was carried out for general practices and CCGs, and organizations were ranked according to the change in prescribing rate. Results: We identified a reduction in total opioid prescribing in 94 (49.2%) out of 191 CCGs, with a median reduction of 15.1 (IQR 11.8-18.7; range 9.0-32.8) in total oral morphine equivalence per 1000 patients. We present data for the 3 CCGs and practices demonstrating the biggest reduction in opioid prescribing for each of the 3 opioid prescribing measures. We observed a 40% proportional drop (8.9% absolute reduction) in the regular prescribing of high-dose opioids (measured as a percentage of regular opioids) in the highest-ranked CCG (North Tyneside); a 99% drop in this same measure was found in several practices (44%-95% absolute reduction). Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over a period of 2 years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time. Conclusions: By applying 1 of our existing analysis tools to a national data set, we were able to identify rapid and maintained changes in opioid prescribing within practices and CCGs and rank organizations by the magnitude of reduction. Highly ranked organizations are candidates for further qualitative research into intervention design and implementation. UR - https://publichealth.jmir.org/2024/1/e51323 UR - http://dx.doi.org/10.2196/51323 UR - http://www.ncbi.nlm.nih.gov/pubmed/38838327 ID - info:doi/10.2196/51323 ER - TY - JOUR AU - Nickel, Brooke AU - Heiss, Raffael AU - Shih, Patti AU - Gram, Grundtvig Emma AU - Copp, Tessa AU - Taba, Melody AU - Moynihan, Ray AU - Zadro, Joshua PY - 2024/6/4 TI - Social Media Promotion of Health Tests With Potential for Overdiagnosis or Overuse: Protocol for a Content Analysis JO - JMIR Res Protoc SP - e56899 VL - 13 KW - social media KW - influencers KW - tests KW - overdiagnosis KW - overuse KW - evidence-based medicine KW - promotion N2 - Background: In recent years, social media have emerged as important spaces for commercial marketing of health tests, which can be used for the screening and diagnosis of otherwise generally healthy people. However, little is known about how health tests are promoted on social media, whether the information provided is accurate and balanced, and if there is transparency around conflicts of interest. Objective: This study aims to understand and quantify how social media is being used to discuss or promote health tests with the potential for overdiagnosis or overuse to generally healthy people. Methods: Content analysis of social media posts on the anti-Mullerian hormone test, whole-body magnetic resonance imaging scan, multicancer early detection, testosterone test, and gut microbe test from influential international social media accounts on Instagram and TikTok. The 5 tests have been identified as having the following criteria: (1) there are evidence-based concerns about overdiagnosis or overuse, (2) there is evidence or concerns that the results of tests do not lead to improved health outcomes for generally healthy people and may cause harm or waste, and (3) the tests are being promoted on social media to generally healthy people. English language text-only posts, images, infographics, articles, recorded videos including reels, and audio-only posts are included. Posts from accounts with <1000 followers as well as stories, live videos, and non-English posts are excluded. Using keywords related to the test, the top posts were searched and screened until there were 100 eligible posts from each platform for each test (total of 1000 posts). Data from the caption, video, and on-screen text are being summarized and extracted into a Microsoft Excel (Microsoft Corporation) spreadsheet and included in the analysis. The analysis will take a combined inductive approach when generating key themes and a deductive approach using a prespecified framework. Quantitative data will be analyzed in Stata SE (version 18.0; Stata Corp). Results: Data on Instagram and TikTok have been searched and screened. Analysis has now commenced. The findings will be disseminated via publications in peer-reviewed international medical journals and will also be presented at national and international conferences in late 2024 and 2025. Conclusions: This study will contribute to the limited evidence base on the nature of the relationship between social media and the problems of overdiagnosis and overuse of health care services. This understanding is essential to develop strategies to mitigate potential harm and plan solutions, with the aim of helping to protect members of the public from being marketed low-value tests, becoming patients unnecessarily, and taking resources away from genuine needs within the health system. International Registered Report Identifier (IRRID): DERR1-10.2196/56899 UR - https://www.researchprotocols.org/2024/1/e56899 UR - http://dx.doi.org/10.2196/56899 UR - http://www.ncbi.nlm.nih.gov/pubmed/38833693 ID - info:doi/10.2196/56899 ER - TY - JOUR AU - Hakariya, Hayase AU - Yokoyama, Natsuki AU - Lee, Jeonse AU - Hakariya, Arisa AU - Ikejiri, Tatsuki PY - 2024/5/28 TI - Illicit Trade of Prescription Medications Through X (Formerly Twitter) in Japan: Cross-Sectional Study JO - JMIR Form Res SP - e54023 VL - 8 KW - illegal trading KW - pharmacovigilance KW - social networking service KW - SNS KW - overdose KW - social support KW - antipsychotics KW - Japan KW - prescription medication KW - cross-sectional study KW - prescription drug KW - social networking KW - medication KW - pharmaceutical KW - pharmaceutical drugs KW - Japanese KW - psychiatric KW - support N2 - Background: Nonmedical use of prescription drugs can cause overdose; this represents a serious public health crisis globally. In this digital era, social networking services serve as viable platforms for illegal acquisition of excessive amounts of medications, including prescription medications. In Japan, such illegal drug transactions have been conducted through popular flea market applications, social media, and auction websites, with most of the trades being over-the-counter (OTC) medications. Recently, an emerging unique black market, where individuals trade prescription medications?predominantly nervous system drugs?using a specific keyword (?Okusuri Mogu Mogu?), has emerged on X (formerly Twitter). Hence, these dynamic methods of illicit trading should routinely be monitored to encourage the appropriate use of medications. Objective: This study aimed to specify the characteristics of medications traded on X using the search term ?Okusuri Mogu Mogu? and analyze individual behaviors associated with X posts, including the types of medications traded and hashtag usage. Methods: We conducted a cross-sectional study with publicly available posts on X between September 18 and October 1, 2022. Posts that included the term ?Okusuri Mogu Mogu? during this period were scrutinized. Posts were categorized on the basis of their contents: buying, selling, self-administration, heads-up, and others. Among posts categorized as buying, selling, and self-administration, medication names were systematically enumerated and categorized using the Anatomical Therapeutic Chemical (ATC) classification. Additionally, hashtags in all the analyzed posts were counted and classified into 6 categories: medication name, mental disorder, self-harm, buying and selling, community formation, and others. Results: Out of 961 identified posts, 549 were included for analysis. Of these posts, 119 (21.7%) referenced self-administration, and 237 (43.2%; buying: n=67, 12.2%; selling: n=170, 31.0%) referenced transactions. Among these 237 posts, 1041 medication names were mentioned, exhibiting a >5-fold increase from the study in March 2021. Categorization based on the ATC classification predominantly revealed nervous system drugs, representing 82.1% (n=855) of the mentioned medications, consistent with the previous survey. Of note, the diversity of medications has expanded to include medications that have not been approved by the Japanese government. Interestingly, OTC medications were frequently mentioned in self-administration posts (odds ratio 23.6, 95% CI 6.93-80.15). Analysis of hashtags (n=866) revealed efforts to foster community connections among users. Conclusions: This study highlighted the escalating complexity of trading of illegal prescription medication facilitated by X posts. Regulatory measures to enhance public awareness should be considered to prevent illegal transactions, which may ultimately lead to misuse or abuse such as overdose. Along with such pharmacovigilance measures, social approaches that could direct individuals to appropriate medical or psychiatric resources would also be beneficial as our hashtag analysis shed light on the formation of a cohesive or closed community among users. UR - https://formative.jmir.org/2024/1/e54023 UR - http://dx.doi.org/10.2196/54023 UR - http://www.ncbi.nlm.nih.gov/pubmed/38805262 ID - info:doi/10.2196/54023 ER - TY - JOUR AU - Yue, Qi-Xuan AU - Ding, Ruo-Fan AU - Chen, Wei-Hao AU - Wu, Lv-Ying AU - Liu, Ke AU - Ji, Zhi-Liang PY - 2024/5/3 TI - Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study JO - J Med Internet Res SP - e48572 VL - 26 KW - clinical drug toxicity KW - adverse drug reaction KW - ADR severity KW - ADR frequency KW - mathematical model N2 - Background: Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. Objective: In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. Methods: In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. Results: The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. Conclusions: In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation. UR - https://www.jmir.org/2024/1/e48572 UR - http://dx.doi.org/10.2196/48572 UR - http://www.ncbi.nlm.nih.gov/pubmed/38700923 ID - info:doi/10.2196/48572 ER - TY - JOUR AU - Leas, C. Eric AU - Ayers, W. John AU - Desai, Nimit AU - Dredze, Mark AU - Hogarth, Michael AU - Smith, M. Davey PY - 2024/5/2 TI - Using Large Language Models to Support Content Analysis: A Case Study of ChatGPT for Adverse Event Detection JO - J Med Internet Res SP - e52499 VL - 26 KW - adverse events KW - artificial intelligence KW - AI KW - text analysis KW - annotation KW - ChatGPT KW - LLM KW - large language model KW - cannabis KW - delta-8-THC KW - delta-8-tetrahydrocannabiol UR - https://www.jmir.org/2024/1/e52499 UR - http://dx.doi.org/10.2196/52499 UR - http://www.ncbi.nlm.nih.gov/pubmed/38696245 ID - info:doi/10.2196/52499 ER - TY - JOUR AU - Makunts, Tigran AU - Joulfayan, Haroutyun AU - Abagyan, Ruben PY - 2024/5/1 TI - Thyroid Hyperplasia and Neoplasm Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists in the Food and Drug Administration Adverse Event Reporting System: Retrospective Analysis JO - JMIRx Med SP - e55976 VL - 5 KW - GLP-1 KW - FDA KW - averse event reporting KW - cancer KW - oncology KW - neoplasm KW - drugs KW - pharmacy KW - pharmacology KW - pharmaceutics KW - medication KW - medications KW - glucagon-like peptide-1 KW - Food and Drug Administration KW - weight loss KW - diabetes KW - obesity KW - thyroid hyperplasia KW - FAERS KW - FDA Adverse Event Reporting System N2 - Background: Glucagon-like peptide-1 (GLP-1) receptor agonists (RAs) are one of the most commonly used drugs for type 2 diabetes mellitus. Clinical guidelines recommend GLP-1 RAs as an adjunct to diabetes therapy in patients with chronic kidney disease, presence or risk of atherosclerotic cardiovascular disease, and obesity. The weight loss observed in clinical trials has been explored further in healthy individuals, putting GLP-1 RAs on track to be the next weight loss treatment. Objective: Although the adverse event profile is relatively safe, most GLP-1 RAs come with a labeled boxed warning for the risk of thyroid cancers, based on animal models and some postmarketing case reports in humans. Considering the increasing popularity of this drug class and its expansion into a new popular indication, a further review of the most recent postmarketing safety data was warranted to quantify thyroid hyperplasia and neoplasm instances. Methods: GLP-1 RA patient reports from the US Food and Drug Administration (FDA) Adverse Event Reporting System database were analyzed using reporting odds ratios and 95% CIs. Results: In this study, we analyzed over 18 million reports from the US FDA Adverse Event Reporting System and provided evidence of significantly increased propensity for thyroid hyperplasias and neoplasms in patients taking GLP-1 RA monotherapy when compared to patients taking sodium-glucose cotransporter-2 (SGLT-2) inhibitor monotherapy. Conclusions: GLP-1 RAs, regardless of indication, are associated with an over 10-fold increase in thyroid neoplasm and hyperplasia adverse event reporting when compared to SGLT-2 inhibitors. UR - https://xmed.jmir.org/2024/1/e55976 UR - http://dx.doi.org/10.2196/55976 ID - info:doi/10.2196/55976 ER - TY - JOUR AU - Chedid, Maroun AU - Chebib, T. Fouad AU - Dahlen, Erin AU - Mueller, Theodore AU - Schnell, Theresa AU - Gay, Melissa AU - Hommos, Musab AU - Swaminathan, Sundararaman AU - Garg, Arvind AU - Mao, Michael AU - Amberg, Brigid AU - Balderes, Kirk AU - Johnson, F. Karen AU - Bishop, Alyssa AU - Vaughn, Kay Jackqueline AU - Hogan, Marie AU - Torres, Vicente AU - Chaudhry, Rajeev AU - Zoghby, Ziad PY - 2024/5/1 TI - An Electronic Health Record?Integrated Application for Standardizing Care and Monitoring Patients With Autosomal Dominant Polycystic Kidney Disease Enrolled in a Tolvaptan Clinic: Design and Implementation Study JO - JMIR Med Inform SP - e50164 VL - 12 KW - ADPKD KW - autosomal dominant polycystic kidney disease KW - polycystic kidney disease KW - tolvaptan KW - EHR KW - electronic health record KW - digital health solutions KW - monitoring KW - kidney disease KW - drug-related toxicity KW - digital application KW - management KW - chronic disease N2 - Background: Tolvaptan is the only US Food and Drug Administration?approved drug to slow the progression of autosomal dominant polycystic kidney disease (ADPKD), but it requires strict clinical monitoring due to potential serious adverse events. Objective: We aimed to share our experience in developing and implementing an electronic health record (EHR)?based application to monitor patients with ADPKD who were initiated on tolvaptan. Methods: The application was developed in collaboration with clinical informatics professionals based on our clinical protocol with frequent laboratory test monitoring to detect early drug-related toxicity. The application streamlined the clinical workflow and enabled our nursing team to take appropriate actions in real time to prevent drug-related serious adverse events. We retrospectively analyzed the characteristics of the enrolled patients. Results: As of September 2022, a total of 214 patients were enrolled in the tolvaptan program across all Mayo Clinic sites. Of these, 126 were enrolled in the Tolvaptan Monitoring Registry application and 88 in the Past Tolvaptan Patients application. The mean age at enrollment was 43.1 (SD 9.9) years. A total of 20 (9.3%) patients developed liver toxicity, but only 5 (2.3%) had to discontinue the drug. The 2 EHR-based applications allowed consolidation of all necessary patient information and real-time data management at the individual or population level. This approach facilitated efficient staff workflow, monitoring of drug-related adverse events, and timely prescription renewal. Conclusions: Our study highlights the feasibility of integrating digital applications into the EHR workflow to facilitate efficient and safe care delivery for patients enrolled in a tolvaptan program. This workflow needs further validation but could be extended to other health care systems managing chronic diseases requiring drug monitoring. UR - https://medinform.jmir.org/2024/1/e50164 UR - http://dx.doi.org/10.2196/50164 ID - info:doi/10.2196/50164 ER - TY - JOUR AU - Nishioka, Satoshi AU - Watabe, Satoshi AU - Yanagisawa, Yuki AU - Sayama, Kyoko AU - Kizaki, Hayato AU - Imai, Shungo AU - Someya, Mitsuhiro AU - Taniguchi, Ryoo AU - Yada, Shuntaro AU - Aramaki, Eiji AU - Hori, Satoko PY - 2024/4/16 TI - Adverse Event Signal Detection Using Patients? Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models JO - J Med Internet Res SP - e55794 VL - 26 KW - cancer KW - anticancer drug KW - adverse event KW - side effect KW - patient-reported outcome KW - patients? voice KW - patient-oriented KW - patient narrative KW - natural language processing KW - deep learning KW - pharmaceutical care record KW - SOAP N2 - Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients? subjective opinions (patients? voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients? narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients? daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients? concerns at pharmacies was also assessed. Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients? concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients? daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. ?Pain or numbness? (n=57, 36.3%), ?fever? (n=46, 29.3%), and ?nausea? (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients? daily lives. Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients? subjective information recorded in pharmaceutical care records accumulated during pharmacists? daily work. UR - https://www.jmir.org/2024/1/e55794 UR - http://dx.doi.org/10.2196/55794 UR - http://www.ncbi.nlm.nih.gov/pubmed/38625718 ID - info:doi/10.2196/55794 ER - TY - JOUR AU - Hebard, Stephen AU - Weaver, GracieLee AU - Hansen, B. William AU - Ruppert, Scarlett PY - 2024/4/12 TI - Evaluation of a Pilot Program to Prevent the Misuse of Prescribed Opioids Among Health Care Workers: Repeated Measures Survey Study JO - JMIR Form Res SP - e53665 VL - 8 KW - health care workers KW - opioid misuse KW - pain management KW - prescription opioids KW - prevention KW - substance abuse KW - substance use KW - workers N2 - Background: Overprescription of opioids has led to increased misuse of opioids, resulting in higher rates of overdose. The workplace can play a vital role in an individual?s intentions to misuse prescription opioids with injured workers being prescribed opioids, at a rate 3 times the national average. For example, health care workers are at risk for injuries, opioid dispensing, and diversion. Intervening within a context that may contribute to risks for opioid misuse while targeting individual psychosocial factors may be a useful complement to interventions at policy and prescribing levels. Objective: This pilot study assessed the effects of a mobile-friendly opioid misuse intervention prototype tailored for health care workers using the preparation phase of a multiphase optimization strategy design. Methods: A total of 33 health care practitioners participated in the pilot intervention, which included 10 brief web-based lessons aimed at impacting psychosocial measures that underlie opioid misuse. The lesson topics included: addiction beliefs, addiction control, Centers for Disease Control and Prevention guidelines and recommendations, beliefs about patient-provider relationships and communication, control in communicating with providers, beliefs about self-monitoring pain and side effects, control in self-monitoring pain and side effects, diversion and disposal beliefs, diversion and disposal control, and a conclusion lesson. Using a treatment-only design, pretest and posttest surveys were collected. A general linear repeated measures ANOVA was used to assess mean differences from pretest to posttest. Descriptive statistics were used to assess participant feedback about the intervention. Results: After completing the intervention, participants showed significant mean changes with increases in knowledge of opioids (+0.459; P<.001), less favorable attitudes toward opioids (?1.081; P=.001), more positive beliefs about communication with providers (+0.205; P=.01), more positive beliefs about pain management control (+0.969; P<.001), and increased intentions to avoid opioid use (+0.212; P=.03). Of the 33 practitioners who completed the program, most felt positive about the information presented, and almost 70% (23/33) agreed or strongly agreed that other workers in the industry should complete a program like this. Conclusions: While attempts to address the opioid crisis have been made through public health policies and prescribing initiatives, opioid misuse continues to rise. Certain industries place workers at greater risk for injury and opioid dispensing, making interventions that target workers in these industries of particular importance. Results from this pilot study show positive impacts on knowledge, attitudes, and beliefs about communicating with providers and pain management control, as well as intentions to avoid opioid misuse. However, the dropout rate and small sample size are severe limitations, and the results lack generalizability. Results will be used to inform program revisions and future optimization trials, with the intention of providing insight for future intervention development and evaluation of mobile-friendly eHealth interventions for employees. UR - https://formative.jmir.org/2024/1/e53665 UR - http://dx.doi.org/10.2196/53665 UR - http://www.ncbi.nlm.nih.gov/pubmed/38607664 ID - info:doi/10.2196/53665 ER - TY - JOUR AU - Lyzwinski, Nathalie Lynnette AU - Elgendi, Mohamed AU - Menon, Carlo PY - 2024/4/11 TI - Users' Acceptability and Perceived Efficacy of mHealth for Opioid Use Disorder: Scoping Review JO - JMIR Mhealth Uhealth SP - e49751 VL - 12 KW - acceptability KW - addict KW - addiction KW - addictions KW - app KW - app-based KW - application KW - applications KW - apps KW - barrier KW - barriers KW - challenge KW - challenges KW - messaging KW - mHealth KW - mobile health KW - monitoring KW - opioid KW - opioids KW - overdose KW - overdosing KW - pharmacology KW - review methodology KW - review methods KW - scoping KW - sensor KW - sensors KW - SMS KW - substance abuse KW - substance use KW - text message KW - wearable technology KW - wearable KW - wearables N2 - Background: The opioid crisis continues to pose significant challenges to global public health, necessitating the development of novel interventions to support individuals in managing their substance use and preventing overdose-related deaths. Mobile health (mHealth), as a promising platform for addressing opioid use disorder, requires a comprehensive understanding of user perspectives to minimize barriers to care and optimize the benefits of mHealth interventions. Objective: This study aims to synthesize qualitative insights into opioid users? acceptability and perceived efficacy of mHealth and wearable technologies for opioid use disorder. Methods: A scoping review of PubMed (MEDLINE) and Google Scholar databases was conducted to identify research on opioid user perspectives concerning mHealth-assisted interventions, including wearable sensors, SMS text messaging, and app-based technology. Results: Overall, users demonstrate a high willingness to engage with mHealth interventions to prevent overdose-related deaths and manage opioid use. Users perceive mHealth as an opportunity to access care and desire the involvement of trusted health care professionals in these technologies. User comfort with wearing opioid sensors emerged as a significant factor. Personally tailored content, social support, and encouragement are preferred by users. Privacy concerns and limited access to technology pose barriers to care. Conclusions: To maximize benefits and minimize risks for users, it is crucial to implement robust privacy measures, provide comprehensive user training, integrate behavior change techniques, offer professional and peer support, deliver tailored messages, incorporate behavior change theories, assess readiness for change, design stigma-reducing apps, use visual elements, and conduct user-focused research for effective opioid management in mHealth interventions. mHealth demonstrates considerable potential as a tool for addressing opioid use disorder and preventing overdose-related deaths, given the high acceptability and perceived benefits reported by users. UR - https://mhealth.jmir.org/2024/1/e49751 UR - http://dx.doi.org/10.2196/49751 UR - http://www.ncbi.nlm.nih.gov/pubmed/38602751 ID - info:doi/10.2196/49751 ER - TY - JOUR AU - Hall, William Eric AU - Sullivan, Sean Patrick AU - Bradley, Heather PY - 2024/4/5 TI - Estimated Number of Injection-Involved Overdose Deaths in US States From 2000 to 2020: Secondary Analysis of Surveillance Data JO - JMIR Public Health Surveill SP - e49527 VL - 10 KW - death rate KW - death KW - drug abuse KW - drugs KW - injection drug use KW - injection KW - mortality KW - National Vital Statistics System KW - overdose death rate KW - overdose KW - state KW - substance abuse KW - Treatment Episode Dataset-Admission KW - treatment N2 - Background: In the United States, both drug overdose mortality and injection-involved drug overdose mortality have increased nationally over the past 25 years. Despite documented geographic differences in overdose mortality and substances implicated in overdose mortality trends, injection-involved overdose mortality has not been summarized at a subnational level. Objective: We aimed to estimate the annual number of injection-involved overdose deaths in each US state from 2000 to 2020. Methods: We conducted a stratified analysis that used data from drug treatment admissions (Treatment Episodes Data Set?Admissions; TEDS-A) and the National Vital Statistics System (NVSS) to estimate state-specific percentages of reported drug overdose deaths that were injection-involved from 2000 to 2020. TEDS-A collects data on the route of administration and the type of substance used upon treatment admission. We used these data to calculate the percentage of reported injections for each drug type by demographic group (race or ethnicity, sex, and age group), year, and state. Additionally, using NVSS mortality data, the annual number of overdose deaths involving selected drug types was identified by the following specific multiple-cause-of-death codes: heroin or synthetic opioids other than methadone (T40.1, T40.4), natural or semisynthetic opioids and methadone (T40.2, T40.3), cocaine (T40.5), psychostimulants with abuse potential (T43.6), sedatives (T42.3, T42.4), and others (T36-T59.0). We used the probabilities of injection with the annual number of overdose deaths, by year, primary substance, and demographic groups to estimate the number of overdose deaths that were injection-involved. Results: In 2020, there were 91,071 overdose deaths among adults recorded in the United States, and 93.1% (84,753/91,071) occurred in the 46 jurisdictions that reported data to TEDS-A. Slightly less than half (38,253/84,753, 45.1%; 95% CI 41.1%-49.8%) of those overdose deaths were estimated to be injection-involved, translating to 38,253 (95% CI 34,839-42,181) injection-involved overdose deaths in 2020. There was large variation among states in the estimated injection-involved overdose death rate (median 14.72, range 5.45-31.77 per 100,000 people). The national injection-involved overdose death rate increased by 323% (95% CI 255%-391%) from 2010 (3.78, 95% CI 3.33-4.31) to 2020 (15.97, 95% CI 14.55-17.61). States in which the estimated injection-involved overdose death rate increased faster than the national average were disproportionately concentrated in the Northeast region. Conclusions: Although overdose mortality and injection-involved overdose mortality have increased dramatically across the country, these trends have been more pronounced in some regions. A better understanding of state-level trends in injection-involved mortality can inform the prioritization of public health strategies that aim to reduce overdose mortality and prevent downstream consequences of injection drug use. UR - https://publichealth.jmir.org/2024/1/e49527 UR - http://dx.doi.org/10.2196/49527 UR - http://www.ncbi.nlm.nih.gov/pubmed/38578676 ID - info:doi/10.2196/49527 ER - TY - JOUR AU - Choo, Mei Sim AU - Sartori, Daniele AU - Lee, Chet Sing AU - Yang, Hsuan-Chia AU - Syed-Abdul, Shabbir PY - 2024/4/3 TI - Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST JO - JMIR Med Inform SP - e49643 VL - 12 KW - pharmacovigilance KW - medication safety KW - big data analysis KW - feature selection KW - interpretable machine learning N2 - Background: The completeness of adverse event (AE) reports, crucial for assessing putative causal relationships, is measured using the vigiGrade completeness score in VigiBase, the World Health Organization global database of reported potential AEs. Malaysian reports have surpassed the global average score (approximately 0.44), achieving a 5-year average of 0.79 (SD 0.23) as of 2019 and approaching the benchmark for well-documented reports (0.80). However, the contributing factors to this relatively high report completeness score remain unexplored. Objective: This study aims to explore the main drivers influencing the completeness of Malaysian AE reports in VigiBase over a 15-year period using vigiGrade. A secondary objective was to understand the strategic measures taken by the Malaysian authorities leading to enhanced report completeness across different time frames. Methods: We analyzed 132,738 Malaysian reports (2005-2019) recorded in VigiBase up to February 2021 split into historical International Drug Information System (INTDIS; n=63,943, 48.17% in 2005-2016) and newer E2B (n=68,795, 51.83% in 2015-2019) format subsets. For machine learning analyses, we performed a 2-stage feature selection followed by a random forest classifier to identify the top features predicting well-documented reports. We subsequently applied tree Shapley additive explanations to examine the magnitude, prevalence, and direction of feature effects. In addition, we conducted time-series analyses to evaluate chronological trends and potential influences of key interventions on reporting quality. Results: Among the analyzed reports, 42.84% (56,877/132,738) were well documented, with an increase of 65.37% (53,929/82,497) since 2015. Over two-thirds (46,186/68,795, 67.14%) of the Malaysian E2B reports were well documented compared to INTDIS reports at 16.72% (10,691/63,943). For INTDIS reports, higher pharmacovigilance center staffing was the primary feature positively associated with being well documented. In recent E2B reports, the top positive features included reaction abated upon drug dechallenge, reaction onset or drug use duration of <1 week, dosing interval of <1 day, reports from public specialist hospitals, reports by pharmacists, and reaction duration between 1 and 6 days. In contrast, reports from product registration holders and other health care professionals and reactions involving product substitution issues negatively affected the quality of E2B reports. Multifaceted strategies and interventions comprising policy changes, continuity of education, and human resource development laid the groundwork for AE reporting in Malaysia, whereas advancements in technological infrastructure, pharmacovigilance databases, and reporting tools concurred with increases in both the quantity and quality of AE reports. Conclusions: Through interpretable machine learning and time-series analyses, this study identified key features that positively or negatively influence the completeness of Malaysian AE reports and unveiled how Malaysia has developed its pharmacovigilance capacity via multifaceted strategies and interventions. These findings will guide future work in enhancing pharmacovigilance and public health. UR - https://medinform.jmir.org/2024/1/e49643 UR - http://dx.doi.org/10.2196/49643 UR - http://www.ncbi.nlm.nih.gov/pubmed/38568722 ID - info:doi/10.2196/49643 ER - TY - JOUR AU - Ashraf, Reza Amir AU - Mackey, Ken Tim AU - Fittler, András PY - 2024/3/21 TI - Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online JO - JMIR Public Health Surveill SP - e53086 VL - 10 KW - generative artificial intelligence KW - artificial intelligence KW - comparative assessment KW - search engines KW - online pharmacies KW - patient safety KW - generative KW - safety KW - search engine KW - search KW - searches KW - searching KW - website KW - websites KW - Google KW - Bing KW - retrieval KW - information seeking KW - illegal KW - pharmacy KW - pharmacies KW - risk KW - risks KW - consumer KW - consumers KW - customer KW - customers KW - recommendation KW - recommendations KW - vendor KW - vendors KW - substance use KW - substance abuse KW - controlled substances KW - controlled substance KW - drug KW - drugs KW - pharmaceutic KW - pharmaceutics KW - pharmaceuticals KW - pharmaceutical KW - medication KW - medications N2 - Background: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. Objective: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. Methods: We conducted a comparative assessment of AI-generated recommendations from Google?s Search Generative Experience (SGE) and Microsoft Bing?s Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. Results: Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat?s and 13.23% (18/136) of Google SGE?s recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). Conclusions: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations. UR - https://publichealth.jmir.org/2024/1/e53086 UR - http://dx.doi.org/10.2196/53086 UR - http://www.ncbi.nlm.nih.gov/pubmed/38512343 ID - info:doi/10.2196/53086 ER - TY - JOUR AU - Oyibo, Kiemute AU - Gonzalez, A. Paola AU - Ejaz, Sarah AU - Naheyan, Tasneem AU - Beaton, Carla AU - O?Donnell, Denis AU - Barker, R. James PY - 2024/3/21 TI - Exploring the Use of Persuasive System Design Principles to Enhance Medication Incident Reporting and Learning Systems: Scoping Reviews and Persuasive Design Assessment JO - JMIR Hum Factors SP - e41557 VL - 11 KW - medication incident KW - reporting system KW - persuasive technology KW - persuasive design KW - medication KW - persuasive system design KW - pharmacy KW - pharmaceutic KW - pharmacology KW - drug reporting KW - drug event KW - adverse event KW - incident management N2 - Background: Medication incidents (MIs) causing harm to patients have far-reaching consequences for patients, pharmacists, public health, business practice, and governance policy. Medication Incident Reporting and Learning Systems (MIRLS) have been implemented to mitigate such incidents and promote continuous quality improvement in community pharmacies in Canada. They aim to collect and analyze MIs for the implementation of incident preventive strategies to increase safety in community pharmacy practice. However, this goal remains inhibited owing to the persistent barriers that pharmacies face when using these systems. Objective: This study aims to investigate the harms caused by medication incidents and technological barriers to reporting and identify opportunities to incorporate persuasive design strategies in MIRLS to motivate reporting. Methods: We conducted 2 scoping reviews to provide insights on the relationship between medication errors and patient harm and the information system?based barriers militating against reporting. Seven databases were searched in each scoping review, including PubMed, Public Health Database, ProQuest, Scopus, ACM Library, Global Health, and Google Scholar. Next, we analyzed one of the most widely used MIRLS in Canada using the Persuasive System Design (PSD) taxonomy?a framework for analyzing, designing, and evaluating persuasive systems. This framework applies behavioral theories from social psychology in the design of technology-based systems to motivate behavior change. Independent assessors familiar with MIRLS reported the degree of persuasion built into the system using the 4 categories of PSD strategies: primary task, dialogue, social, and credibility support. Results: Overall, 17 articles were included in the first scoping review, and 1 article was included in the second scoping review. In the first review, significant or serious harm was the most frequent harm (11/17, 65%), followed by death or fatal harm (7/17, 41%). In the second review, the authors found that iterative design could improve the usability of an MIRLS; however, data security and validation of reports remained an issue to be addressed. Regarding the MIRLS that we assessed, participants considered most of the primary task, dialogue, and credibility support strategies in the PSD taxonomy as important and useful; however, they were not comfortable with some of the social strategies such as cooperation. We found that the assessed system supported a number of persuasive strategies from the PSD taxonomy; however, we identified additional strategies such as tunneling, simulation, suggestion, praise, reward, reminder, authority, and verifiability that could further enhance the perceived persuasiveness and value of the system. Conclusions: MIRLS, equipped with persuasive features, can become powerful motivational tools to promote safer medication practices in community pharmacies. They have the potential to highlight the value of MI reporting and increase the readiness of pharmacists to report incidents. The proposed persuasive design guidelines can help system developers and community pharmacy managers realize more effective MIRLS. UR - https://humanfactors.jmir.org/2024/1/e41557 UR - http://dx.doi.org/10.2196/41557 UR - http://www.ncbi.nlm.nih.gov/pubmed/38512325 ID - info:doi/10.2196/41557 ER - TY - JOUR AU - Rizvi, Fatima Rubina AU - Schoephoerster, Ann Jamee AU - Desphande, Satish Sagar AU - Usher, Michael AU - Oien, Elaine Andy AU - Peters, Marie Maya AU - Loth, Scott Matthew AU - Bahr, William Matthew AU - Ventz, Steffen AU - Koopmeiners, Stephen Joseph AU - Melton, B. Genevieve PY - 2024/3/8 TI - Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e52882 VL - 13 KW - opioid abuse KW - opioid naïve patients KW - opioid addiction KW - behavioral economics KW - nudges KW - MyChart KW - personal health record KW - post-operative care KW - opioid KW - opioid use KW - randomized controlled trial KW - RCT KW - digital engagement KW - health crisis KW - overdose KW - acute pain KW - pain KW - tool KW - tools KW - phone app KW - website KW - application N2 - Background: Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted. Objective: Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting. The primary goal of this project is to evaluate the effect of the 3 versions of the Opioid Management for You (OPY) tool on measures of opioid use relative to the standard of care by leveraging a pragmatic randomized controlled trial (RCT). Methods: A team of researchers from the Center for Learning Health System Sciences (CLHSS) at the University of Minnesota partnered with M Health Fairview to design, build, and test the 3 versions of the OPY tool: social influence, precommitment, and testimonial version. The tool is being built using the Epic Care Companion (Epic Inc) platform and interacts with the patient through their existing MyChart (Epic Systems Corporation) personal health record account, and Epic patient portal, accessed through a phone app or the MyChart website. We have demonstrated feasibility with pilot data of the social influence version of the OPY app by targeting our pilot to a specific cohort of patients undergoing upper-extremity procedures. This study will use a group sequential RCT design to test the impact of this important health system initiative. Patients who meet OPY inclusion criteria will be stratified into low, intermediate, and high risk of opiate use based on their type of surgery. Results: This study is being funded and supported by the CLHSS Rapid Prospective Evaluation and Digital Technology Innovation Programs, and M Health Fairview. Support and coordination provided by CLHSS include the structure of engagement, survey development, data collection, statistical analysis, and dissemination. The project was initially started in August 2022. The pilot was launched in February 2023 and is still running, with the data last counted in August 2023. The actual RCT is planned to start by early 2024. Conclusions: Through this RCT, we will test our hypothesis that patient opioid use and diverted prescription opioid availability can both be improved by information delivery applied through a behavioral economics lens via sending nudges directly to the opioid users through their personal health record. Trial Registration: ClinicalTrials.gov NCT06124079; https://clinicaltrials.gov/study/NCT06124079 International Registered Report Identifier (IRRID): PRR1-10.2196/52882 UR - https://www.researchprotocols.org/2024/1/e52882 UR - http://dx.doi.org/10.2196/52882 UR - http://www.ncbi.nlm.nih.gov/pubmed/38457203 ID - info:doi/10.2196/52882 ER - TY - JOUR AU - Ni, Chenxu AU - Wang, Yi-fu AU - Zhang, Yun-ting AU - Yuan, Min AU - Xu, Qing AU - Shen, Fu-ming AU - Li, Dong-Jie AU - Huang, Fang PY - 2024/2/29 TI - A Mobile Applet for Assessing Medication Adherence and Managing Adverse Drug Reactions Among Patients With Cancer: Usability and Utility Study JO - JMIR Form Res SP - e50528 VL - 8 KW - WeChat applet KW - usability testing KW - utility testing KW - cancer patients KW - patients KW - cancer KW - qualitative study N2 - Background: Medication adherence and the management of adverse drug reactions (ADRs) are crucial to the efficacy of antitumor drugs. A WeChat applet, also known as a ?Mini Program,? is similar to the app but has marked advantages. The development and use of a WeChat applet makes follow-up convenient for patients with cancer. Objective: This study aimed to assess the usability and utility of a newly developed WeChat applet, ?DolphinCare,? among patients with cancer in Shanghai. Methods: A qualitative methodology was used to obtain an in-depth understanding of the experiences of patients with cancer when using DolphinCare from the usability and utility aspects. The development phase consisted of 2 parts: alpha and beta testing. Alpha testing combined the theory of the Fogg Behavior Model and the usability model. Alpha testing also involved testing the design of DolphinCare using a conceptual framework, which included factors that could affect medication adherence and ADRs. Beta testing was conducted using in-depth interviews. In-depth interviews allowed us to assist the patients in using DolphinCare and understand whether they liked or disliked DolphinCare and found it useful. Results: We included participants who had an eHealth Literacy Scale (eHEALS) score of ?50%, and a total of 20 participants were interviewed consecutively. The key positive motivators described by interviewers were to be reminded to take their medications and to alleviate their ADRs. The majority of the patients were able to activate and use DolphinCare by themselves. Most patients indicated that their trigger to follow-up DolphinCare was the recommendation of their known and trusted health care professionals. All participants found that labels containing the generic names of their medication and the medication reminders were useful, including timed pop-up push notifications and text alerts. The applet presented the corresponding information collection forms of ADRs to the patient to fill out. The web-based consultation system enables patients to consult pharmacists or physicians in time when they have doubts about medications or have ADRs. The applet had usabilities and utilities that could improve medication adherence and the management of ADRs among patients with cancer. Conclusions: This study provides preliminary evidence regarding the usability and utility of this type of WeChat applet among patients with cancer, which is expected to be promoted for managing follow-up among other patients with other chronic disease. UR - https://formative.jmir.org/2024/1/e50528 UR - http://dx.doi.org/10.2196/50528 UR - http://www.ncbi.nlm.nih.gov/pubmed/38421700 ID - info:doi/10.2196/50528 ER - TY - JOUR AU - ElSherief, Mai AU - Sumner, Steven AU - Krishnasamy, Vikram AU - Jones, Christopher AU - Law, Royal AU - Kacha-Ochana, Akadia AU - Schieber, Lyna AU - De Choudhury, Munmun PY - 2024/2/23 TI - Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study JO - JMIR Form Res SP - e44726 VL - 8 KW - addiction treatment KW - machine learning KW - misinformation KW - natural language processing KW - opioid use disorder KW - social media KW - substance use N2 - Background: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. Objective: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD. Methods: Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation. Results: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. Conclusions: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content. UR - https://formative.jmir.org/2024/1/e44726 UR - http://dx.doi.org/10.2196/44726 UR - http://www.ncbi.nlm.nih.gov/pubmed/38393772 ID - info:doi/10.2196/44726 ER - TY - JOUR AU - Jeon, Min Soo AU - Lim, HyunJoo AU - Cheon, Hyo-bin AU - Ryu, Juhee AU - Kwon, Jin-Won PY - 2024/1/30 TI - Assessing the Labeling Information on Drugs Associated With Suicide Risk: Systematic Review JO - JMIR Public Health Surveill SP - e49755 VL - 10 KW - suicide KW - adverse drug events KW - review KW - drug KW - mental health KW - systematic review KW - drug induced suicide KW - drug reaction KW - substance use KW - suicidal KW - medication KW - suicide symptoms KW - suicidal risk KW - drugs KW - adverse drug event N2 - Background: Drug-induced suicide (DIS) is a severe adverse drug reaction (ADR). Although clinical trials have provided evidence on DIS, limited investigations have been performed on rare ADRs, such as suicide. Objective: We aimed to systematically review case reports on DIS to provide evidence-based drug information. Methods: We searched PubMed to obtain case reports regarding DIS published until July 2021. Cases resulting from drugs that are no longer used or are nonapproved, substance use, and suicidal intentions were excluded. The quality of each case report was assessed using the CASE (Case Reports) checklist. We extracted data regarding demographics, medication history, suicide symptoms, and symptom improvement and evaluated the causality of DIS using the Naranjo score. Furthermore, to identify the potential suicidal risk of the unknown drugs, we compared the results of the causality assessment with those of the approved drug labels. Results: In 83 articles, we identified 152 cases involving 61 drugs. Antidepressants were reported as the most frequent causative drugs of DIS followed by immunostimulants. The causality assessment revealed 61 cases having possible, 89 cases having probable, and 2 cases having definite relationships with DIS. For approximately 85% of suspected drugs, the risk of suicidal ADRs was indicated on the approved label; however, the approved labels for 9 drugs, including lumacaftor/ivacaftor, doxycycline, clozapine, dextromethorphan, adalimumab, infliximab, piroxicam, paclitaxel, and formoterol, did not provide information about these risks. Conclusions: We found several case reports involving drugs without suicide risk information on the drug label. Our findings might provide valuable insights into drugs that may cause suicidal ADRs. UR - https://publichealth.jmir.org/2024/1/e49755 UR - http://dx.doi.org/10.2196/49755 UR - http://www.ncbi.nlm.nih.gov/pubmed/38289650 ID - info:doi/10.2196/49755 ER - TY - JOUR AU - Lau, Y. Erica AU - Cragg, Amber AU - Small, S. Serena AU - Butcher, Katherine AU - Hohl, M. Corinne PY - 2024/1/18 TI - Characterizing and Comparing Adverse Drug Events Documented in 2 Spontaneous Reporting Systems in the Lower Mainland of British Columbia, Canada: Retrospective Observational Study JO - JMIR Hum Factors SP - e52495 VL - 11 KW - adverse drug event reporting systems KW - side effect KW - side effects KW - drug KW - drugs KW - pharmacy KW - pharmacology KW - pharmacotherapy KW - pharmaceutic KW - pharmaceutics KW - pharmaceuticals KW - pharmaceutical KW - medication KW - medications KW - patient safety KW - health information technology KW - pharmacovigilance KW - adverse KW - safety KW - HIT KW - information system KW - information systems KW - reporting KW - descriptive statistics KW - monitoring N2 - Background: Robust adverse drug event (ADE) reporting systems are crucial to monitor and identify drug safety signals, but the quantity and type of ADEs captured may vary by system characteristics. Objective: We compared ADEs reported in 2 different reporting systems in the same jurisdictions, the Patient Safety and Learning System?Adverse Drug Reaction (PSLS-ADR) and ActionADE, to understand report variation. Methods: This retrospective observational study analyzed reports entered into PSLS-ADR and ActionADE systems between December 1, 2019, and December 31, 2022. We conducted a comprehensive analysis including all events from both reporting systems to examine coverage and usage and understand the types of events captured in both systems. We calculated descriptive statistics for reporting facility type, patient demographics, serious events, and most reported drugs. We conducted a subanalysis focused on adverse drug reactions to enable direct comparisons between systems in terms of the volume and events reported. We stratified results by reporting system. Results: We performed the comprehensive analysis on 3248 ADE reports, of which 12.4% (375/3035) were reported in PSLS-ADR and 87.6% (2660/3035) were reported in ActionADE. Distribution of all events and serious events varied slightly between the 2 systems. Iohexol, gadobutrol, and empagliflozin were the most common culprit drugs (173/375, 46.2%) in PSLS-ADR, while hydrochlorothiazide, apixaban, and ramipril (308/2660, 11.6%) were common in ActionADE. We included 2728 reports in the subanalysis of adverse drug reactions, of which 12.9% (353/2728) were reported in PSLS-ADR and 86.4% (2357/2728) were reported in ActionADE. ActionADE captured 4- to 6-fold more comparable events than PSLS-ADR over this study?s period. Conclusions: User-friendly and robust reporting systems are vital for pharmacovigilance and patient safety. This study highlights substantial differences in ADE data that were generated by different reporting systems. Understanding system factors that lead to varying reporting patterns can enhance ADE monitoring and should be taken into account when evaluating drug safety signals. UR - https://humanfactors.jmir.org/2024/1/e52495 UR - http://dx.doi.org/10.2196/52495 UR - http://www.ncbi.nlm.nih.gov/pubmed/38236629 ID - info:doi/10.2196/52495 ER - TY - JOUR AU - Guilcher, T. Sara J. AU - Cimino, R. Stephanie AU - Tadrous, Mina AU - McCarthy, M. Lisa AU - Riad, Jessica AU - Tricco, C. Andrea AU - Hagens, Simon AU - Lien, Jennifer AU - Tharmalingam, Sukirtha AU - Gomes, Tara PY - 2023/12/28 TI - Experiences and Outcomes of Using e-Prescribing for Opioids: Rapid Scoping Review JO - J Med Internet Res SP - e49173 VL - 25 KW - e-prescribing KW - opioid prescription KW - opioid use KW - rapid scoping review N2 - Background: e-Prescribing is designed to assist in facilitating safe and appropriate prescriptions for patients. Currently, it is unknown to what extent e-prescribing for opioids influences experiences and outcomes. To address this gap, a rapid scoping review was conducted. Objective: This rapid scoping review aims to (1) explore how e-prescribing has been used clinically; (2) examine the effects of e-prescribing on clinical outcomes, the patient or clinician experience, service delivery, and policy; and (3) identify current gaps in the present literature to inform future studies and recommendations. Methods: A rapid scoping review was conducted following the guidance of the JBI 2020 scoping review methodology and the World Health Organization guide to rapid reviews. A comprehensive literature search was completed by an expert librarian from inception until November 16, 2022. Three databases were electronically searched: MEDLINE (Ovid), Embase (Ovid), and Scopus (Elsevier). The search criteria were as follows: (1) e-prescribing programs targeted to the use or misuse of opioids, including those that were complemented or accompanied by clinically focused initiatives, and (2) a primary research study of experimental, quasi-experimental, observational, qualitative, or mixed methods design. An additional criterion of an ambulatory component of e-prescribing (eg, e-prescribing occurred upon discharge from acute care) was added at the full-text stage. No language limitations or filters were applied. All articles were double screened by trained reviewers. Gray literature was manually searched by a single reviewer. Data were synthesized using a descriptive approach. Results: Upon completing screening, 34 articles met the inclusion criteria: 32 (94%) peer-reviewed studies and 2 (6%) gray literature documents (1 thesis study and 1 report). All 33 studies had a quantitative component, with most highlighting e-prescribing from acute care settings to community settings (n=12, 36%). Only 1 (3%) of the 34 articles provided evidence on e-prescribing in a primary care setting. Minimal prescriber, pharmacist, and clinical population characteristics were reported. The main outcomes identified were related to opioid prescribing rates, alerts (eg, adverse drug events and drug-drug interactions), the quantity and duration of opioid prescriptions, the adoption of e-prescribing technology, attitudes toward e-prescribing, and potential challenges with the implementation of e-prescribing into clinical practice. e-Prescribing, including key features such as alerts and dose order sets, may reduce prescribing errors. Conclusions: This rapid scoping review highlights initial promising results with e-prescribing and opioid therapy management. It is important that future work explores the experience of prescribers, pharmacists, and patients using e-prescribing for opioid therapy management with an emphasis on prescribers in the community and primary care. Developing a common set of quality indicators for e-prescribing of opioids will help build a stronger evidence base. Understanding implementation considerations will be of importance as the technology is integrated into clinical practice and health systems. UR - https://www.jmir.org/2023/1/e49173 UR - http://dx.doi.org/10.2196/49173 UR - http://www.ncbi.nlm.nih.gov/pubmed/38153776 ID - info:doi/10.2196/49173 ER - TY - JOUR AU - Ben-Aharon, Irit AU - Rotem, Ran AU - Melzer-Cohen, Cheli AU - Twig, Gilad AU - Cercek, Andrea AU - Half, Elizabeth AU - Goshen-Lago, Tal AU - Chodik, Gabriel AU - Kelsen, David PY - 2023/12/13 TI - Pharmaceutical Agents as Potential Drivers in the Development of Early-Onset Colorectal Cancer: Case-Control Study JO - JMIR Public Health Surveill SP - e50110 VL - 9 KW - early onset colorectal cancer KW - pharmaceutical agents KW - increased risk KW - colorectal cancer KW - health provider KW - Israel KW - machine learning KW - decision tree KW - gradient boosting KW - risk factors KW - decision support KW - risk KW - risks KW - colorectal KW - cancer KW - oncology KW - internal medicine KW - gastroenterology KW - gastrointestinal KW - pharmaceutical KW - pharmaceuticals KW - drug KW - drugs N2 - Background: The incidence of early-onset colorectal cancer (EOCRC) rose abruptly in the mid 1990s, is continuing to increase, and has now been noted in many countries. By 2030, 25% of American patients diagnosed with rectal cancer will be 49 years or younger. The large majority of EOCRC cases are not found in patients with germline cancer susceptibility mutations (eg, Lynch syndrome) or inflammatory bowel disease. Thus, environmental or lifestyle factors are suspected drivers. Obesity, sedentary lifestyle, diabetes mellitus, smoking, alcohol, or antibiotics affecting the gut microbiome have been proposed. However, these factors, which have been present since the 1950s, have not yet been conclusively linked to the abrupt increase in EOCRC. The sharp increase suggests the introduction of a new risk factor for young people. We hypothesized that the driver may be an off-target effect of a pharmaceutical agent (ie, one requiring regulatory approval before its use in the general population or an off-label use of a previously approved agent) in a genetically susceptible subgroup of young adults. If a pharmaceutical agent is an EOCRC driving factor, regulatory risk mitigation strategies could be used. Objective: We aimed to evaluate the possibility that pharmaceutical agents serve as risk factors for EOCRC. Methods: We conducted a case-control study. Data including demographics, comorbidities, and complete medication dispensing history were obtained from the electronic medical records database of Maccabi Healthcare Services, a state-mandated health provider covering 26% of the Israeli population. The participants included 941 patients with EOCRC (?50 years of age) diagnosed during 2001-2019 who were density matched at a ratio of 1:10 with 9410 control patients. Patients with inflammatory bowel disease and those with a known inherited cancer susceptibility syndrome were excluded. An advanced machine learning algorithm based on gradient boosted decision trees coupled with Bayesian model optimization and repeated data sampling was used to sort through the very high-dimensional drug dispensing data to identify specific medication groups that were consistently linked with EOCRC while allowing for synergistic or antagonistic interactions between medications. Odds ratios for the identified medication classes were obtained from a conditional logistic regression model. Results: Out of more than 800 medication classes, we identified several classes that were consistently associated with EOCRC risk across independently trained models. Interactions between medication groups did not seem to substantially affect the risk. In our analysis, drug groups that were consistently positively associated with EOCRC included beta blockers and valerian (Valeriana officinalis). Antibiotics were not consistently associated with EOCRC risk. Conclusions: Our analysis suggests that the development of EOCRC may be correlated with prior use of specific medications. Additional analyses should be used to validate the results. The mechanism of action inducing EOCRC by candidate pharmaceutical agents will then need to be determined. UR - https://publichealth.jmir.org/2023/1/e50110 UR - http://dx.doi.org/10.2196/50110 UR - http://www.ncbi.nlm.nih.gov/pubmed/37933755 ID - info:doi/10.2196/50110 ER - TY - JOUR AU - Turvey, Carolyn AU - Fuhrmeister, Lindsey AU - Klein, Dawn AU - McCoy, Kimberly AU - Moeckli, Jane AU - Stewart Steffensmeier, R. Kenda AU - Suiter, Natalie AU - Van Tiem, Jen PY - 2023/12/8 TI - Secure Messaging Intervention in Patients Starting New Antidepressant to Promote Adherence: Pilot Randomized Controlled Trial JO - JMIR Form Res SP - e51277 VL - 7 KW - depression KW - text messaging KW - medication adherence KW - medication KW - medications KW - adherence KW - antidepressant KW - antidepressants KW - depressive KW - text message KW - text messages KW - messaging KW - SMS KW - veteran KW - veterans KW - military KW - randomized controlled trial KW - RCT KW - controlled trials KW - mental health KW - psychiatry KW - mobile phone N2 - Background: There are a range of effective pharmacological and behavioral treatments for depression. However, approximately one-third of patients discontinue antidepressants within the first month of treatment and 44% discontinue them by the third month of treatment. The major reasons reported for discontinuation were side effect burden, patients experiencing that the medications were not working, and patients wanting to resolve their depression without using medication. Objective: This study tested the acceptability, feasibility, and preliminary effectiveness of an SMS messaging intervention designed to improve antidepressant adherence and depression outcomes in veterans. The intervention specifically targeted the key reasons for antidepressant discontinuation. For example, the secure message included reminders that it can take up to 6 weeks for an antidepressant to work, or prompts to call their provider should the side effect burden become significant. Methods: This pilot was a 3-armed randomized controlled trial of 53 veterans undergoing depression treatment at the Iowa City Veterans Affairs Health Care System. Veterans starting a new antidepressant were randomized to secure messaging only (SM-Only), secure messaging with coaching (SM+Coach), or attention control (AC) groups. The intervention lasted 12 weeks with follow-up assessments of key outcomes at 6 and 12-weeks. This included a measure of antidepressant adherence, depressive symptom severity, and side effect burden. Results: The 2 active interventions (SM-Only and SM+Coach) demonstrated small to moderate effect sizes (ESs) in improving antidepressant adherence and reducing side effect burden. They did not appear to reduce the depressive symptom burden any more than in the AC arm. Veteran participants in the SM arms demonstrated improved medication adherence from baseline to 12 weeks on the Medication Adherence Rating Scale compared with those in the AC arm, who had a decline in adherence (SM-Only: ES=0.09; P=.19; SM+Coach: ES=0.85; P=.002). Depression scores on the 9-Item Patient Health Questionnaire decreased for all 3 treatment arms, although the decline was slightly larger for the SM-Only (ES=0.32) and the SM+Coach (ES=0.24) arms when compared with the AC arm. The 2 intervention arms indicated a decrease in side effects on the Frequency, Intensity, and Burden of Side Effects Ratings, whereas the side effect burden for the AC arm increased. These differences indicated moderate ES (SM-Only vs AC: ES=0.40; P=.07; SM+Coach: ES=0.54; P=.07). Conclusions: A secure messaging program targeting specific reasons for antidepressant discontinuation had small-to-moderate ES in improving medication adherence. Consistent with prior research, the intervention that included brief synchronic meetings with a coach appeared to have a greater benefit than the SMS-alone intervention. Veterans consistently engaged with the SMS messaging in both treatment arms throughout the study period. They additionally provided feedback on which texts were most helpful, tending to prefer messages providing overall encouragement rather than specific wellness recommendations. Trial Registration: ClinicalTrials.gov NCT03930849; https://clinicaltrials.gov/study/NCT03930849 UR - https://formative.jmir.org/2023/1/e51277 UR - http://dx.doi.org/10.2196/51277 UR - http://www.ncbi.nlm.nih.gov/pubmed/38064267 ID - info:doi/10.2196/51277 ER - TY - JOUR AU - Kariya, Azusa AU - Okada, Hiroshi AU - Suzuki, Shota AU - Dote, Satoshi AU - Nishikawa, Yoshitaka AU - Araki, Kazuo AU - Takahashi, Yoshimitsu AU - Nakayama, Takeo PY - 2023/11/22 TI - Internet-Based Inquiries From Users With the Intention to Overdose With Over-the-Counter Drugs: Qualitative Analysis of Yahoo! Chiebukuro JO - JMIR Form Res SP - e45021 VL - 7 KW - abuse KW - consumer-generated media KW - CGM KW - overdose KW - over-the-counter drug KW - OTC drug KW - question and answer site KW - Q and A site N2 - Background: Public concern with regard to over-the-counter (OTC) drug abuse is growing rapidly across countries. OTC drug abuse has serious effects on the mind and body, such as poisoning symptoms, and often requires specialized treatments. In contrast, there is concern about people who potentially abuse OTC drugs whose symptoms are not serious enough to consult medical institutions or drug addiction rehabilitation centers yet are at high risk of becoming drug dependent in the future. Objective: Consumer-generated media (CGM), which allows users to disseminate information, is being used by people who abuse (and those who are trying to abuse) OTC drugs to obtain information about OTC drug abuse. This study aims to analyze the content of CGM to explore the questions of people who potentially abuse OTC drugs. Methods: The subject of this research was Yahoo! Chiebukuro, the largest question and answer website in Japan. A search was performed using the names of drugs commonly used in OTC drug abuse and the keywords overdose and OD, and the number of questions posted on the content of OTC drug abuse was counted. Furthermore, a thematic analysis was conducted by extracting text data on the most abused antitussive and expectorant drug, BRON. Results: The number of questions about the content of overdose medications containing the keyword BRON has increased sharply as compared with other product names. Furthermore, 467 items of question data that met the eligibility criteria were obtained from 528 items of text data on BRON; 26 codes, 6 categories, and 3 themes were generated from the 578 questions contained in these items. Questions were asked about the effects they would gain from abusing OTC drugs and the information they needed to obtain the effects they sought, as well as about the effects of abuse on their bodies. Moreover, there were questions on how to stop abusing and what is needed when seeking help from a health care provider if they become dependent. It has become clear that people who abuse OTC drugs have difficulty in consulting face-to-face with others, and CGM is used as a means to obtain the necessary information anonymously. Conclusions: On CGM, people who abused or tried to abuse OTC drugs were asking questions about their abuse expectations and anxieties. In addition, when they became dependent, they sought advice to quit their abuse. CGM was used to exchange information about OTC drug abuse, and many questions on anxieties and hesitations were posted. This study suggests that it is necessary to produce and disseminate information on OTC drug abuse, considering the situation of those who abuse or are willing to abuse OTC drugs. Support from pharmacies and drugstores would also be essential to reduce opportunities for OTC drug abuse. UR - https://formative.jmir.org/2023/1/e45021 UR - http://dx.doi.org/10.2196/45021 UR - http://www.ncbi.nlm.nih.gov/pubmed/37991829 ID - info:doi/10.2196/45021 ER - TY - JOUR AU - Carabot, Federico AU - Fraile-Martínez, Oscar AU - Donat-Vargas, Carolina AU - Santoma, Javier AU - Garcia-Montero, Cielo AU - Pinto da Costa, Mariana AU - Molina-Ruiz, M. Rosa AU - Ortega, A. Miguel AU - Alvarez-Mon, Melchor AU - Alvarez-Mon, Angel Miguel PY - 2023/10/31 TI - Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study JO - J Med Internet Res SP - e50013 VL - 25 KW - awareness KW - epidemic KW - fentanyl KW - health communication KW - infodemiology KW - machine learning KW - opioids KW - recreational use KW - social media listening KW - Twitter KW - user N2 - Background: Opioids are used for the treatment of refractory pain, but their inappropriate use has detrimental consequences for health. Understanding the current experiences and perceptions of patients in a spontaneous and colloquial environment regarding the key drugs involved in the opioid crisis is of utmost significance. Objective: The study aims to analyze Twitter content related to opioids, with objectives including characterizing users participating in these conversations, identifying prevalent topics and gauging public perception, assessing opinions on drug efficacy and tolerability, and detecting discussions related to drug dispensing, prescription, or acquisition. Methods: In this cross-sectional study, we gathered public tweets concerning major opioids posted in English or Spanish between January 1, 2019, and December 31, 2020. A total of 256,218 tweets were collected. Approximately 27% (69,222/256,218) were excluded. Subsequently, 7000 tweets were subjected to manual analysis based on a codebook developed by the researchers. The remaining databases underwent analysis using machine learning classifiers. In the codebook, the type of user was the initial classification domain. We differentiated between patients, family members and friends, health care professionals, and institutions. Next, a distinction was made between medical and nonmedical content. If it was medical in nature, we classified it according to whether it referred to the drug?s efficacy or adverse effects. In nonmedical content tweets, we analyzed whether the content referred to management issues (eg, pharmacy dispensation, medical appointment prescriptions, commercial advertisements, or legal aspects) or the trivialization of the drug. Results: Among the entire array of scrutinized pharmaceuticals, fentanyl emerged as the predominant subject, featuring in 27% (39,997/148,335 posts) of the tweets. Concerning user categorization, roughly 70% (101,259/148,335) were classified as patients. Nevertheless, tweets posted by health care professionals obtained the highest number of retweets (37/16,956, 0.2% of their posts received over 100 retweets). We found statistically significant differences in the distribution concerning efficacy and side effects among distinct drug categories (P<.001). Nearly 60% (84,401/148,335) of the posts were devoted to nonmedical subjects. Within this category, legal facets and recreational use surfaced as the most prevalent themes, while in the medical discourse, efficacy constituted the most frequent topic, with over 90% (45,621/48,777) of instances characterizing it as poor or null. The opioid with the greatest proportion of tweets concerning legal considerations was fentanyl. Furthermore, fentanyl was the drug most frequently offered for sale on Twitter, while methadone generated the most tweets about pharmacy delivery. Conclusions: The opioid crisis is present on social media, where tweets discuss legal and recreational use. Opioid users are the most active participants, prioritizing medication efficacy over side effects. Surprisingly, health care professionals generate the most engagement, indicating their positive reception. Authorities must monitor web-based opioid discussions to detect illicit acquisitions and recreational use. UR - https://www.jmir.org/2023/1/e50013 UR - http://dx.doi.org/10.2196/50013 UR - http://www.ncbi.nlm.nih.gov/pubmed/37906234 ID - info:doi/10.2196/50013 ER - TY - JOUR AU - Oreskovic, Jessica AU - Kaufman, Jaycee AU - Thommandram, Anirudh AU - Fossat, Yan PY - 2023/10/24 TI - A Radar-Based Opioid Overdose Detection Device for Public Restrooms: Design, Development, and Evaluation Study JO - JMIR Biomed Eng SP - e51754 VL - 8 KW - 60 GHz radar KW - opioid overdose KW - overdose detection KW - overdose prevention KW - respiratory depression N2 - Background: The opioid epidemic is a growing crisis worldwide. While many interventions have been put in place to try to protect people from opioid overdoses, they typically rely on the person to take initiative in protecting themselves, requiring forethought, preparation, and action. Respiratory depression or arrest is the mechanism by which opioid overdoses become fatal, but it can be reversed with the timely administration of naloxone. Objective: In this study, we described the development and validation of an opioid overdose detection radar (ODR), specifically designed for use in public restroom stalls. In-laboratory testing was conducted to validate the noncontact, privacy-preserving device against a respiration belt and to determine the accuracy and reliability of the device. Methods: We used an ODR system with a high-frequency pulsed coherent radar sensor and a Raspberry Pi (Raspberry Pi Ltd), combining advanced technology with a compact and cost-effective setup to monitor respiration and detect opioid overdoses. To determine the optimal position for the ODR within the confined space of a restroom stall, iterative testing was conducted, considering the radar?s bounded capture area and the limitations imposed by the stall?s dimensions and layout. By adjusting the orientation of the ODR, we were able to identify the most effective placement where the device reliably tracked respiration in a number of expected positions. Experiments used a mock restroom stall setup that adhered to building code regulations, creating a controlled environment while maintaining the authenticity of a public restroom stall. By simulating different body positions commonly associated with opioid overdoses, the ODR?s ability to accurately track respiration in various scenarios was assessed. To determine the accuracy of the ODR, testing was performed using a respiration belt as a reference. The radar measurements were compared with those obtained from the belt in experiments where participants were seated upright and slumped over. Results: The results demonstrated favorable agreement between the radar and belt measurements, with an overall mean error in respiration cycle duration of 0.0072 (SD 0.54) seconds for all recorded respiration cycles (N=204). During the simulated overdose experiments where participants were slumped over, the ODR successfully tracked respiration with a mean period difference of 0.0091 (SD 0.62) seconds compared with the reference data. Conclusions: The findings suggest that the ODR has the potential to detect significant deviations in respiration patterns that may indicate an opioid overdose event. The success of the ODR in these experiments indicates the device should be further developed and implemented to enhance safety and emergency response measures in public restrooms. However, additional validation is required for unhealthy opioid-influenced respiratory patterns to guarantee the ODR?s effectiveness in real-world overdose situations. UR - https://biomedeng.jmir.org/2023/1/e51754 UR - http://dx.doi.org/10.2196/51754 UR - http://www.ncbi.nlm.nih.gov/pubmed/38875668 ID - info:doi/10.2196/51754 ER - TY - JOUR AU - Xia, Ting AU - Picco, Louisa AU - Lalic, Samanta AU - Buchbinder, Rachelle AU - Bell, Simon J. AU - Andrew, E. Nadine AU - Lubman, I. Dan AU - Pearce, Christopher AU - Nielsen, Suzanne PY - 2023/10/17 TI - Determining the Impact of Opioid Policy on Substance Use and Mental Health?Related Harms: Protocol for a Data Linkage Study JO - JMIR Res Protoc SP - e51825 VL - 12 KW - data linkage KW - drug policy KW - general practice KW - opioid KW - primary care N2 - Background: Increasing harms related to prescription opioids over the past decade have led to the introduction of a range of key national and state policy initiatives across Australia. These include introducing a mandatory real-time prescription drug?monitoring program in the state of Victoria from April 2020 and a series of changes to subsidies for opioids on the Pharmaceutical Benefit Scheme from June 2020. Together, these changes aim to influence opioid supply and reduce harms related to prescription opioids, yet few studies have specifically explored how these policies have influenced opioid prescribing and related harms in Australia. Objective: The aim of this study is to examine the impact of a range of opioid-related policies on hospital admissions and emergency department (ED) presentations in Victoria, Australia. In particular, the study aims to understand the effect of various opioid policies and opioid-prescribing changes on (1) the number and rates of ED presentations and hospital admissions attributed to substance use (ie, opioid and nonopioid related) or mental ill-health (eg, suicide, self-harm, anxiety, and depression), (2) the association between differing opioid dose trajectories and the likelihood of ED presentations and hospital admissions related to substance use and mental ill-health, and (3) whether changes in an individual?s opioid prescribing change the risk related to ED presentations and hospital admissions related to substance use and mental ill-health. Methods: We will conduct a population-level linked data study. General practice health records obtained from the Population Level Analysis and Reporting platform are linked with person-level data from 3 large hospital networks in Victoria, Australia. Interrupted time series analysis will be used to examine the impact of opioid policies on a range of harms, including the rates of presentations related to substance use (opioid and nonopioid) and mental ill-health among the primary care cohort. Group-based trajectory modeling and a case-crossover design will be used to further explore the impact of changes in opioid dosage and other covariates on opioid and nonopioid poisonings and mental ill-health?related presentations at the patient level. Results: Given that this paper serves as a protocol, there are currently no results available. The deidentified primary health data were sourced from electronic medical records of approximately 4,717,000 patients from 542 consenting general practices over a 6-year period (2017-2022). The submission of results for publication is planned for early 2024. Conclusions: This study will add to the limited evidence base to help understand the impact of opioid policies in Australia, including whether intended or unintended outcomes are occurring as a result. Trial Registration: EU PAS Register EUPAS104005; https://www.encepp.eu/encepp/viewResource.htm?id=104006 International Registered Report Identifier (IRRID): DERR1-10.2196/51825 UR - https://www.researchprotocols.org/2023/1/e51825 UR - http://dx.doi.org/10.2196/51825 UR - http://www.ncbi.nlm.nih.gov/pubmed/37847553 ID - info:doi/10.2196/51825 ER - TY - JOUR AU - Korb-Savoldelli, Virginie AU - Tran, Yohann AU - Perrin, Germain AU - Touchard, Justine AU - Pastre, Jean AU - Borowik, Adrien AU - Schwartz, Corine AU - Chastel, Aymeric AU - Thervet, Eric AU - Azizi, Michel AU - Amar, Laurence AU - Kably, Benjamin AU - Arnoux, Armelle AU - Sabatier, Brigitte PY - 2023/10/16 TI - Psychometric Properties of a Machine Learning?Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study JO - J Med Internet Res SP - e42384 VL - 25 KW - medication adherence KW - long-term therapies KW - machine learning KW - patient-reported outcome measure KW - decision tree KW - predict KW - Delphi KW - cross sectional KW - psychometric KW - mobile phone N2 - Background: Medication adherence plays a critical role in controlling the evolution of chronic disease, as low medication adherence may lead to worse health outcomes, higher mortality, and morbidity. Assessment of their patients' medication adherence by clinicians is essential for avoiding inappropriate therapeutic intensification, associated health care expenditures, and the inappropriate inclusion of patients in time- and resource-consuming educational interventions. In both research and clinical practices the most extensively used measures of medication adherence are patient-reported outcome measures (PROMs), because of their ability to capture subjective dimensions of nonadherence. Machine learning (ML), a subfield of artificial intelligence, uses computer algorithms that automatically improve through experience. In this context, ML tools could efficiently model the complexity of and interactions between multiple patient behaviors that lead to medication adherence. Objective: This study aimed to create and validate a PROM on medication adherence interpreted using an ML approach. Methods: This cross-sectional, single-center, observational study was carried out a French teaching hospital between 2021 and 2022. Eligible patients must have had at least 1 long-term treatment, medication adherence evaluation other than a questionnaire, the ability to read or understand French, an age older than 18 years, and provided their nonopposition. Included adults responded to an initial version of the PROM composed of 11 items, each item being presented using a 4-point Likert scale. The initial set of items was obtained using a Delphi consensus process. Patients were classified as poorly, moderately, or highly adherent based on the results of a medication adherence assessment standard used in the daily practice of each outpatient unit. An ML-derived decision tree was built by combining the medication adherence status and PROM responses. Sensitivity, specificity, positive and negative predictive values (NPVs), and global accuracy of the final 5-item PROM were evaluated. Results: We created an initial 11-item PROM with a 4-point Likert scale using the Delphi process. After item reduction, a decision tree derived from 218 patients including data obtained from the final 5-item PROM allowed patient classification into poorly, moderately, or highly adherent based on item responses. The psychometric properties were 78% (95% CI 40%-96%) sensitivity, 71% (95% CI 53%-85%) specificity, 41% (95% CI 19%-67%) positive predictive values, 93% (95% CI 74%-99%) NPV, and 70% (95% CI 55%-83%) accuracy. Conclusions: We developed a medication adherence tool based on ML with an excellent NPV. This could allow prioritization processes to avoid referring highly adherent patients to time- and resource-consuming interventions. The decision tree can be easily implemented in computerized prescriber order-entry systems and digital tools in smartphones. External validation of this tool in a study including a larger number of patients with diseases associated with low medication adherence is required to confirm its use in analyzing and assessing the complexity of medication adherence. UR - https://www.jmir.org/2023/1/e42384 UR - http://dx.doi.org/10.2196/42384 UR - http://www.ncbi.nlm.nih.gov/pubmed/37843891 ID - info:doi/10.2196/42384 ER - TY - JOUR AU - Fossouo Tagne, Joel AU - Yakob, Amin Reginald AU - Mcdonald, Rachael AU - Wickramasinghe, Nilmini PY - 2023/9/29 TI - A Web-Based Tool to Report Adverse Drug Reactions by Community Pharmacists in Australia: Usability Testing Study JO - JMIR Form Res SP - e48976 VL - 7 KW - ADR KW - adverse drug reaction KW - pharmacovigilance KW - community pharmacy KW - digital health evaluation KW - usability testing N2 - Background: Adverse drug reactions (ADRs) are unintended and harmful events associated with medication use. Despite their significance in postmarketing surveillance, quality improvement, and drug safety research, ADRs are vastly underreported. Enhanced digital-based communication of ADR information to regulators and among care providers could significantly improve patient safety. Objective: This paper presents a usability evaluation of the commercially available GuildCare Adverse Event Recording system, a web-based ADR reporting system widely used by community pharmacists (CPs) in Australia. Methods: We developed a structured interview protocol encompassing remote observation, think-aloud moderating techniques, and retrospective questioning to gauge the overall user experience, complemented by the System Usability Scale (SUS) assessment. Thematic analysis was used to analyze field notes from the interviews. Results: A total of 7 CPs participated in the study, who perceived the system to have above-average usability (SUS score of 68.57). Nonetheless, the structured approach to usability testing unveiled specific functional and user interpretation issues, such as unnecessary information, lack of system clarity, and redundant data fields?critical insights not captured by the SUS results. Design elements like drop-down menus, free-text entry, checkboxes, and prefilled or auto-populated data fields were perceived as useful for enhancing system navigation and facilitating ADR reporting. Conclusions: The user-centric design of technology solutions, like the one discussed herein, is crucial to meeting CPs? information needs and ensuring effective ADR reporting. Developers should adopt a structured approach to usability testing during the developmental phase to address identified issues comprehensively. Such a methodological approach may promote the adoption of ADR reporting systems by CPs and ultimately enhance patient safety. UR - https://formative.jmir.org/2023/1/e48976 UR - http://dx.doi.org/10.2196/48976 UR - http://www.ncbi.nlm.nih.gov/pubmed/37773620 ID - info:doi/10.2196/48976 ER - TY - JOUR AU - Schleyer, Titus AU - Robinson, Bill AU - Parmar, Samir AU - Janowiak, Diane AU - Gibson, Joseph P. AU - Spangler, Val PY - 2023/9/28 TI - Toxicology Test Results for Public Health Surveillance of the Opioid Epidemic: Retrospective Analysis JO - Online J Public Health Inform SP - e50936 VL - 15 KW - opioid epidemic KW - clinical laboratory techniques KW - public health KW - epidemiology KW - toxicology N2 - Background: Addressing the opioid epidemic requires timely insights into population-level factors, such as trends in prevalence of legal and illegal substances, overdoses, and deaths. Objective: This study aimed to examine whether toxicology test results of living individuals from a variety of sources could be useful in surveilling the opioid epidemic. Methods: A retrospective analysis standardized, merged, and linked toxicology results from 24 laboratories in Marion County, Indiana, United States, from September 1, 2018, to August 31, 2019. The data set consisted of 33,787 Marion County residents and their 746,681 results. We related the data to general Marion County demographics and compared alerts generated by toxicology results to opioid overdose?related emergency department visits. Nineteen domain experts helped prototype analytical visualizations. Main outcome measures included test positivity in the county and by ZIP code; selected demographics of individuals with toxicology results; and correlation of toxicology results with opioid overdose?related emergency department visits. Results: Four percent of Marion County residents had at least 1 toxicology result. Test positivity rates ranged from 3% to 19% across ZIP codes. Males were underrepresented in the data set. Age distribution resembled that of Marion County. Alerts for opioid toxicology results were not correlated with opioid overdose?related emergency department visits. Conclusions: Analyzing toxicology results at scale was impeded by varying data formats, completeness, and representativeness; changes in data feeds; and patient matching difficulties. In this study, toxicology results did not predict spikes in opioid overdoses. Larger, more rigorous and well-controlled studies are needed to assess the utility of toxicology tests in predicting opioid overdose spikes. UR - https://ojphi.jmir.org/2023/1/e50936 UR - http://dx.doi.org/10.2196/50936 UR - http://www.ncbi.nlm.nih.gov/pubmed/38046561 ID - info:doi/10.2196/50936 ER - TY - JOUR AU - Belhassen, Manon AU - Nolin, Maeva AU - Jacoud, Flore AU - Marant Micallef, Claire AU - Van Ganse, Eric PY - 2023/9/26 TI - Trajectories of Controller Therapy Use Before and After Asthma-Related Hospitalization in Children and Adults: Population-Based Retrospective Cohort Study JO - JMIR Public Health Surveill SP - e50085 VL - 9 KW - asthma KW - hospitalization KW - inhaled corticosteroids KW - trajectories KW - quality of care KW - clustering N2 - Background: Inappropriate use of inhaled corticosteroids (ICSs) for asthma impairs control and may cause exacerbation, including asthma-related hospitalization (ARH). In prospective studies, ICS use peaked around ARH, but information on routine care use is limited. Since ARH is a major outcome, controller therapy use in routine care before and after ARH should be documented. Objective: This study aimed to distinguish ICS use typologies (trajectories) before and after ARH, and assess their relationships with sociodemographic, disease, and health care characteristics. Methods: A retrospective cohort study was performed using a 1% random sample of the French claims database. All patients hospitalized for asthma between January 01, 2013, and December 31, 2015, were classified as either children (aged 1-10 years) or teens/adults (aged ?11 years). Health care resource use was assessed between 24 and 12 months before ARH. ICS use was computed with the Continuous Measures of Medication Acquisition-7 (CMA7) for the 4 quarters before and after ARH. Initially, the overall impact of hospitalization on the CMA7 value was studied using a segmented regression analysis in both children and teens/adults. Then, group-based trajectory modeling differentiated the groups with similar ICS use. We tested different models having 2 to 5 distinct trajectory groups before selecting the most appropriate trajectory form. We finally selected the model with the lowest Bayesian Information Criterion, the highest proportion of patients in each group, and the maximum estimated probability of assignment to a specific group. Results: Overall, 863 patients were included in the final study cohort, of which 447 (51.8%) were children and 416 (48.2%) were teens/adults. In children, the average CMA7 value was 12.6% at the start of the observation period, and there was no significant quarter-to-quarter change in the value (P=.14) before hospitalization. Immediately after hospitalization, the average CMA7 value rose by 34.9% (P=.001), before a significant decrease (P=.01) of 7.0% per quarter. In teens/adults, the average CMA7 value was 31.0% at the start, and there was no significant quarter-to-quarter change in the value (P=.08) before hospitalization. Immediately after hospitalization, the average CMA7 value rose by 26.9% (P=.002), before a significant decrease (P=.01) of 7.0% per quarter. We identified 3 and 5 trajectories before ARH in children and adults, respectively, and 5 after ARH for both groups. Trajectories were related to sociodemographic characteristics (particularly, markers of social deprivation) and to potentially inappropriate health care, such as medical management and choice of therapy. Conclusions: Although ARH had an overall positive impact on ICS use trajectories, the effect was often transient, and patient behaviors were heterogeneous. Along with overall trends, distinct trajectories were identified, which were related to specific patients and health care characteristics. Our data reinforce the evidence that inappropriate use of ICS paves the way for ARH. UR - https://publichealth.jmir.org/2023/1/e50085 UR - http://dx.doi.org/10.2196/50085 UR - http://www.ncbi.nlm.nih.gov/pubmed/37751244 ID - info:doi/10.2196/50085 ER - TY - JOUR AU - Fisher, Andrew AU - Young, Maclaren Matthew AU - Payer, Doris AU - Pacheco, Karen AU - Dubeau, Chad AU - Mago, Vijay PY - 2023/9/19 TI - Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework JO - J Med Internet Res SP - e43630 VL - 25 KW - early warning system KW - social media KW - law enforcement KW - public health KW - new psychoactive substances KW - development KW - drug KW - dosage KW - Canada KW - Twitter KW - poisoning KW - monitoring KW - community KW - public safety KW - machine learning KW - Fleiss KW - tweet KW - tweet annotations KW - pharmacology KW - addiction N2 - Background: A hallmark of unregulated drug markets is their unpredictability and constant evolution with newly introduced substances. People who use drugs and the public health workforce are often unaware of the appearance of new drugs on the unregulated market and their type, safe dosage, and potential adverse effects. This increases risks to people who use drugs, including the risk of unknown consumption and unintentional drug poisoning. Early warning systems (EWSs) can help monitor the landscape of emerging drugs in a given community by collecting and tracking up-to-date information and determining trends. However, there are currently few ways to systematically monitor the appearance and harms of new drugs on the unregulated market in Canada. Objective: The goal of this work is to examine how artificial intelligence can assist in identifying patterns of drug-related risks and harms, by monitoring the social media activity of public health and law enforcement groups. This information is beneficial in the form of an EWS as it can be used to identify new and emerging drug trends in various communities. Methods: To collect data for this study, 145 relevant Twitter accounts throughout Quebec (n=33), Ontario (n=78), and British Columbia (n=34) were manually identified. Tweets posted between August 23 and December 21, 2021, were collected via the application programming interface developed by Twitter for a total of 40,393 tweets. Next, subject matter experts (1) developed keyword filters that reduced the data set to 3746 tweets and (2) manually identified relevant tweets for monitoring and early warning efforts for a total of 464 tweets. Using this information, a zero-shot classifier was applied to tweets from step 1 with a set of keep (drug arrest, drug discovery, and drug report) and not-keep (drug addiction support, public safety report, and others) labels to see how accurately it could extract the tweets identified in step 2. Results: When looking at the accuracy in identifying relevant posts, the system extracted a total of 584 tweets and had an overlap of 392 out of 477 (specificity of ~84.5%) with the subject matter experts. Conversely, the system identified a total of 3162 irrelevant tweets and had an overlap of 3090 (sensitivity of ~94.1%) with the subject matter experts. Conclusions: This study demonstrates the benefits of using artificial intelligence to assist in finding relevant tweets for an EWS. The results showed that it can be quite accurate in filtering out irrelevant information, which greatly reduces the amount of manual work required. Although the accuracy in retaining relevant information was observed to be lower, an analysis showed that the label definitions can impact the results significantly and would therefore be suitable for future work to refine. Nonetheless, the performance is promising and demonstrates the usefulness of artificial intelligence in this domain. UR - https://www.jmir.org/2023/1/e43630 UR - http://dx.doi.org/10.2196/43630 UR - http://www.ncbi.nlm.nih.gov/pubmed/37725410 ID - info:doi/10.2196/43630 ER - TY - JOUR AU - Golder, Su AU - O'Connor, Karen AU - Wang, Yunwen AU - Gonzalez Hernandez, Graciela PY - 2023/8/2 TI - The Role of Social Media for Identifying Adverse Drug Events Data in Pharmacovigilance: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e47068 VL - 12 KW - adverse event KW - pharmacovigilance KW - social media KW - real-world data KW - scoping review KW - protocol KW - review method KW - pharmacology KW - pharmaceutics KW - pharmacy KW - adverse drug event KW - adverse drug reaction N2 - Background: Adverse drug events (ADEs) are a considerable public health burden resulting in disability, hospitalization, and death. Even those ADEs deemed nonserious can severely impact a patient?s quality of life and adherence to intervention. Monitoring medication safety, however, is challenging. Social media may be a useful adjunct for obtaining real-world data on ADEs. While many studies have been undertaken to detect adverse events on social media, a consensus has not yet been reached as to the value of social media in pharmacovigilance or its role in pharmacovigilance in relation to more traditional data sources. Objective: The aim of the study is to evaluate and characterize the use of social media in ADE detection and pharmacovigilance as compared to other data sources. Methods: A scoping review will be undertaken. We will search 11 bibliographical databases as well as Google Scholar, hand-searching, and forward and backward citation searching. Records will be screened in Covidence by 2 independent reviewers at both title and abstract stage as well as full text. Studies will be included if they used any type of social media (such as Twitter or patient forums) to detect any type of adverse event associated with any type of medication and then compared the results from social media to any other data source (such as spontaneous reporting systems or clinical literature). Data will be extracted using a data extraction sheet piloted by the authors. Important data on the types of methods used (such as machine learning), any limitations of the methods used, types of adverse events and drugs searched for and included, availability of data and code, details of the comparison data source, and the results and conclusions will be extracted. Results: We will present descriptive summary statistics as well as identify any patterns in the types and timing of ADEs detected, including but not limited to the similarities and differences in what is reported, gaps in the evidence, and the methods used to extract ADEs from social media data. We will also summarize how the data from social media compares to conventional data sources. The literature will be organized by the data source for comparison. Where possible, we will analyze the impact of the types of adverse events, the social media platform used, and the methods used. Conclusions: This scoping review will provide a valuable summary of a large body of research and important information for pharmacovigilance as well as suggest future directions of further research in this area. Through the comparisons with other data sources, we will be able to conclude the added value of social media in monitoring adverse events of medications, in terms of type of adverse events and timing. International Registered Report Identifier (IRRID): PRR1-10.2196/47068 UR - https://www.researchprotocols.org/2023/1/e47068 UR - http://dx.doi.org/10.2196/47068 UR - http://www.ncbi.nlm.nih.gov/pubmed/37531158 ID - info:doi/10.2196/47068 ER - TY - JOUR AU - Zita, Sophia AU - Broussard, Lindsey AU - Hugh, Jeremy AU - Newman, Sabrina PY - 2023/7/20 TI - Cyclosporine in the Treatment of Drug Reaction With Eosinophilia and Systemic Symptoms Syndrome: Retrospective Cohort Study JO - JMIR Dermatol SP - e41391 VL - 6 KW - drug reaction with eosinophilia and systemic symptoms KW - drug-induced hypersensitivity syndrome KW - drug reactions KW - eosinophils KW - cyclosporine KW - treatment KW - skin KW - rash KW - dermatology KW - drug reaction KW - adverse reaction KW - eosinophil KW - eosinophilia KW - Systemic Symptoms KW - drug-induced KW - drugs KW - cohort study KW - case series KW - pharmaceutic KW - pharmacology KW - pharmacy N2 - Background: Drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome is a severe, life-threatening reaction to a culprit drug that frequently involves end-organ damage. Corticosteroids are the first-line treatment for DRESS syndrome; however, corticosteroids may be contraindicated in certain patient populations. There are currently only 54 cases detailing the use of cyclosporine for the treatment of DRESS syndrome reported in the literature. Objective: The aim of this case series was to examine the treatment of DRESS syndrome with cyclosporine in a large patient cohort by aggregating time to symptom resolution, recurrence rate, and treatment dose and duration. Methods: This study was a retrospective cohort study. Patients diagnosed with DRESS syndrome by a board-certified dermatologist and treated at the University of Colorado Hospital from 2015 to 2019 were included. Results: Our inclusion criterion was met by 19 occurrences of DRESS syndrome. With a short course of cyclosporine, 17 of 19 patients in our cohort (89%) had resolution of symptoms (mean treatment length of 5.26 days). DRESS syndrome?s relapse after treatment with cyclosporine occurred in 3 of 19 (16%) occurrences of the cohort. Conclusions: Our study supports the use of cyclosporine in the treatment of DRESS syndrome, particularly in patients who are unable to sustain prolonged immunosuppression. Further research is necessary to compare the efficacy of cyclosporine to the current standard of care in a larger study population and investigate long-term outcomes. UR - https://derma.jmir.org/2023/1/e41391 UR - http://dx.doi.org/10.2196/41391 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632913 ID - info:doi/10.2196/41391 ER - TY - JOUR AU - Schulz, Johannes Peter AU - Crosignani, Francesca AU - Petrocchi, Serena PY - 2023/7/17 TI - Critical Test of the Beneficial Consequences of Lifting the Ban on Direct-to-Consumer Advertising for Prescription Drugs in Italy: Experimental Exposure and Questionnaire Study JO - J Med Internet Res SP - e40616 VL - 25 KW - eDTCA KW - health literacy KW - knowledge KW - empowerment KW - health information KW - antidepressant KW - depression KW - depressive disorder KW - pharmaceutical KW - advertise KW - advertising KW - drug KW - marketing KW - patient education KW - consumer KW - health education N2 - Background: There are only two countries in the world (the United States and New Zealand) that allow the pharmaceutical branch to advertise prescription medication directly to consumers. There is pressure on governments to allow direct-to-consumer advertising (DTCA) for prescription drugs elsewhere too. One argument the industry uses frequently is the claim that exposure to DCTA, through various methods and occasions, is supposed to improve customers? knowledge of a disease and treatment. This argument has been part of the health care community?s wider discussion of whether DTCA of prescription drugs benefits the population?s general interest or is only an attempt to increase the sales of the pharmaceutical branch. Belief in true learning by DTCA is rooted in concepts of empowered consumers and their autonomous and empowered decision-making. Objective: In this study, we tested the hypotheses that contact with DTCA increases recipients? literacy/knowledge, especially regarding the side effects of treatment (hypothesis 1), and empowerment (hypothesis 2). We further hypothesized that DTCA exposure would not increase depression knowledge (ie, about treatments, symptoms, and prevalence) (hypothesis 3). Methods: A snowball sample of 180 participants was randomly split into three experimental groups receiving (1) a traditional information sheet, (2) a DTCA video clip for an antidepressant prescription drug, or (3) both. The video was original material from the United States translated into Italian for the experiment. Dependent variables were measures of depression knowledge (regarding treatments, symptoms and prevalence, and antidepressant side effects), depression literacy, and empowerment. Results: None of the experimental groups differed significantly from the others in the empowerment measure (hypothesis 2 not confirmed). Partial confirmation of hypothesis 1 was obtained. Lower values on the depression literacy scale were obtained when participants had been given the video compared to the sheet condition. However, the general depression knowledge and its subscale on side effects reached higher scores when participants were exposed to the DTCA, alone or in combination with the information sheet. Finally, participants showed lower scores on knowledge about treatment and symptoms or prevalence after watching the video compared to the sheet condition (hypothesis 3 confirmed). Symptoms and prevalence knowledge increased only when the video was presented in combination with the sheet. Conclusions: There is no evidence for an increase in empowerment following DTCA exposure. An increase in knowledge of the side effects of the medication was observed in the group exposed to the DTCA video. This was the only result that confirmed the hypothesis of the beneficial effect of DTCA videos on knowledge. Written information proved to be the most suitable way to convey knowledge on treatments and symptoms prevalence. Our findings support the necessity of studying health literacy and patient empowerment together and the consequences of such an increase in knowledge in terms of help-seeking behavior. UR - https://www.jmir.org/2023/1/e40616 UR - http://dx.doi.org/10.2196/40616 UR - http://www.ncbi.nlm.nih.gov/pubmed/37459159 ID - info:doi/10.2196/40616 ER - TY - JOUR AU - Thai-Van, Hung AU - Valnet-Rabier, Marie-Blanche AU - Anciaux, Maëva AU - Lambert, Aude AU - Maurier, Anaïs AU - Cottin, Judith AU - Pietri, Tessa AU - Destère, Alexandre AU - Damin-Pernik, Marlène AU - Perrouin, Fanny AU - Bagheri, Haleh PY - 2023/7/14 TI - Safety Signal Generation for Sudden Sensorineural Hearing Loss Following Messenger RNA COVID-19 Vaccination: Postmarketing Surveillance Using the French Pharmacovigilance Spontaneous Reporting Database JO - JMIR Public Health Surveill SP - e45263 VL - 9 KW - mRNA COVID-19 vaccine KW - COVID-19 KW - messenger RNA KW - tozinameran KW - elasomeran KW - sudden sensorineural hearing loss KW - audiogram KW - positive rechallenge KW - spontaneous reporting KW - postmarketing KW - surveillance KW - pharmacovigilance N2 - Background: The World Health Organization recently described sudden sensorineural hearing loss (SSNHL) as a possible adverse effect of COVID-19 vaccines. Recent discordant pharmacoepidemiologic studies invite robust clinical investigations of SSNHL after COVID-19 messenger RNA (mRNA) vaccines. This postmarketing surveillance study, overseen by French public health authorities, is the first to clinically document postvaccination SSNHL and examine the role of potential risk factors. Objective: This nationwide study aimed to assess the relationship between SSNHL and exposure to mRNA COVID-19 vaccines and estimate the reporting rate (Rr) of SSNHL after mRNA vaccination per 1 million doses (primary outcome). Methods: We performed a retrospective review of all suspected cases of SSNHL after mRNA COVID-19 vaccination spontaneously reported in France between January 2021 and February 2022 based on a comprehensive medical evaluation, including the evaluation of patient medical history, side and range of hearing loss, and hearing recovery outcomes after a minimum period of 3 months. The quantification of hearing loss and assessment of hearing recovery outcomes were performed according to a grading system modified from the Siegel criteria. A cutoff of 21 days was used for the delay onset of SSNHL. The primary outcome was estimated using the total number of doses of each vaccine administered during the study period in France as the denominator. Results: From 400 extracted cases for tozinameran and elasomeran, 345 (86.3%) spontaneous reports were selected. After reviewing complementary data, 49.6% (171/345) of documented cases of SSNHL were identified. Of these, 83% (142/171) of SSNHL cases occurred after tozinameran vaccination: Rr=1.45/1,000,000 injections; no difference for the rank of injections; complete recovery in 22.5% (32/142) of cases; median delay onset before day 21=4 days (median age 51, IQR 13-83 years); and no effects of sex. A total of 16.9% (29/171) of SSNHL cases occurred after elasomeran vaccination: Rr=1.67/1,000,000 injections; rank effect in favor of the first injection (P=.03); complete recovery in 24% (7/29) of cases; median delay onset before day 21=8 days (median age 47, IQR 33-81 years); and no effects of sex. Autoimmune, cardiovascular, or audiovestibular risk factors were present in approximately 29.8% (51/171) of the cases. SSNHL was more often unilateral than bilateral for both mRNA vaccines (P<.001 for tozinameran; P<.003 for elasomeran). There were 13.5% (23/142) of cases of profound hearing loss, among which 74% (17/23) did not recover a serviceable ear. A positive rechallenge was documented for 8 cases. Conclusions: SSNHL after COVID-19 mRNA vaccines are very rare adverse events that do not call into question the benefits of mRNA vaccines but deserve to be known given the potentially disabling impact of sudden deafness. Therefore, it is essential to properly characterize postinjection SSNHL, especially in the case of a positive rechallenge, to provide appropriate individualized recommendations. UR - https://publichealth.jmir.org/2023/1/e45263 UR - http://dx.doi.org/10.2196/45263 UR - http://www.ncbi.nlm.nih.gov/pubmed/37071555 ID - info:doi/10.2196/45263 ER - TY - JOUR AU - Calvo-Cidoncha, Elena AU - Verdinelli, Julián AU - González-Bueno, Javier AU - López-Soto, Alfonso AU - Camacho Hernando, Concepción AU - Pastor-Duran, Xavier AU - Codina-Jané, Carles AU - Lozano-Rubí, Raimundo PY - 2023/7/10 TI - An Ontology-Based Approach to Improving Medication Appropriateness in Older Patients: Algorithm Development and Validation Study JO - JMIR Med Inform SP - e45850 VL - 11 KW - biological ontologies KW - decision support systems KW - inappropriate prescribing KW - elderly KW - medication regimen complexity KW - anticholinergic drug burden KW - trigger tool KW - clinical KW - ontologies KW - pharmacy KW - medication KW - decision support KW - pharmaceutic KW - pharmacology KW - chronic condition KW - chronic disease KW - domain KW - adverse event KW - ontology-based KW - alert N2 - Background: Inappropriate medication in older patients with multimorbidity results in a greater risk of adverse drug events. Clinical decision support systems (CDSSs) are intended to improve medication appropriateness. One approach to improving CDSSs is to use ontologies instead of relational databases. Previously, we developed OntoPharma?an ontology-based CDSS for reducing medication prescribing errors. Objective: The primary aim was to model a domain for improving medication appropriateness in older patients (chronic patient domain). The secondary aim was to implement the version of OntoPharma containing the chronic patient domain in a hospital setting. Methods: A 4-step process was proposed. The first step was defining the domain scope. The chronic patient domain focused on improving medication appropriateness in older patients. A group of experts selected the following three use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events. The second step was domain model representation. The implementation was conducted by medical informatics specialists and clinical pharmacists using Protégé-OWL (Stanford Center for Biomedical Informatics Research). The third step was OntoPharma-driven alert module adaptation. We reused the existing framework based on SPARQL to query ontologies. The fourth step was implementing the version of OntoPharma containing the chronic patient domain in a hospital setting. Alerts generated from July to September 2022 were analyzed. Results: We proposed 6 new classes and 5 new properties, introducing the necessary changes in the ontologies previously created. An alert is shown if the Medication Regimen Complexity Index is ?40, if the Drug Burden Index is ?1, or if there is a trigger based on an abnormal laboratory value. A total of 364 alerts were generated for 107 patients; 154 (42.3%) alerts were accepted. Conclusions: We proposed an ontology-based approach to provide support for improving medication appropriateness in older patients with multimorbidity in a scalable, sustainable, and reusable way. The chronic patient domain was built based on our previous research, reusing the existing framework. OntoPharma has been implemented in clinical practice and generates alerts, considering the following use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events. UR - https://medinform.jmir.org/2023/1/e45850 UR - http://dx.doi.org/10.2196/45850 ID - info:doi/10.2196/45850 ER - TY - JOUR AU - Salazar, Alejandro AU - Moreno-Pulido, Soledad AU - Prego-Meleiro, Pablo AU - Henares-Montiel, Jesús AU - Pulido, José AU - Donat, Marta AU - Sotres-Fernandez, Gabriel AU - Sordo, Luis PY - 2023/6/28 TI - Correlation Between Opioid Drug Prescription and Opioid-Related Mortality in Spain as a Surveillance Tool: Ecological Study JO - JMIR Public Health Surveill SP - e43776 VL - 9 KW - opioid KW - overdose KW - drug overdose KW - opioid-related deaths KW - mortality KW - tramadol KW - fentanyl KW - substance use KW - substance misuse KW - substance abuse KW - ecological study KW - death N2 - Background: Opioid drug prescription (ODP) and opioid-related mortality (ORM) have increased in Spain. However, their relationship is complex, as ORM is registered without considering the type of opioid (legal or illegal). Objective: This ecological study aimed to examine the correlation between ODP and ORM in Spain and discuss their usefulness as a surveillance tool. Methods: This was an ecological descriptive study using retrospective annual data (2000-2019) from the Spanish general population. Data were collected from people of all ages. Information on ODP was obtained from the Spanish Medicines Agency in daily doses per 1000 inhabitants per day (DHD) for total ODP, total ODP excluding those with better safety protocols (codeine and tramadol), and each opioid drug separately. Rates of ORM (per 1,000,000 inhabitants) were calculated based on deaths registered (International Classification of Diseases, 10th Revision codes) as opioid poisoning by the National Statistics Institute, derived from the drug data recorded by medical examiners in death certificates. Opioid-related deaths were considered to be those that indicated opioid consumption (accidental, infringed, or self-inflicted) as the main cause of death: death due to accidental poisoning (X40-X44), intentional self-inflicted poisoning (X60-X64), drug-induced aggression (X85), and poisoning of undetermined intention (Y10-Y14). A descriptive analysis was carried out, and correlations between the annual rates of ORM and DHD of the prescribed opioid drugs globally, excluding medications of the least potential risk of overdose and lowest treatment tier, were analyzed using Pearson linear correlation coefficient. Their temporal evolution was analyzed using cross-correlations with 24 lags and the cross-correlation function. The analyses were carried out using Stata and StatGraphics Centurion 19. Results: The rate of ORM (2000-2019) ranged between 14 and 23 deaths per 1,000,000 inhabitants, with a minimum in 2006 and an increasing trend starting in 2010. The ODP ranged between 1.51 to 19.94 DHD. The rates of ORM were directly correlated with the DHD of total ODP (r=0.597; P=.006), total ODP without codeine and tramadol (r=0.934; P<.001), and every prescribed opioid except buprenorphine (P=.47). In the time analysis, correlations between DHD and ORM were observed in the same year, although not statistically significant (all P?.05). Conclusions: There is a correlation between greater availability of prescribed opioid drugs and an increase in opioid-related deaths. The correlation between ODP and ORM may be a useful tool in monitoring legal opiates and possible disturbances in the illegal market. The role of tramadol (an easily prescribed opioid) is important in this correlation, as is that of fentanyl (the strongest opioid). Measures stronger than recommendations need to be taken to reduce off-label prescribing. This study shows that not only is opioid use directly related to the prescribing of opioid drugs above what is desirable but also an increase in deaths. UR - https://publichealth.jmir.org/2023/1/e43776 UR - http://dx.doi.org/10.2196/43776 UR - http://www.ncbi.nlm.nih.gov/pubmed/37379061 ID - info:doi/10.2196/43776 ER - TY - JOUR AU - Alexa, Maria Jennifer AU - Richter, Matthias AU - Bertsche, Thilo PY - 2023/6/21 TI - Enhancing Evidence-Based Pharmacy by Comparing the Quality of Web-Based Information Sources to the EVInews Database: Randomized Controlled Trial With German Community Pharmacists JO - J Med Internet Res SP - e45582 VL - 25 KW - databases KW - electronic information KW - evidence-based pharmacy practice KW - evidence-based pharmacy KW - evidence-based practice KW - external evidence KW - health information quality KW - information tools KW - newsletter KW - online survey KW - pharmacist KW - self-medication counseling KW - self-medication KW - utilization N2 - Background: Self-medication counseling in community pharmacies plays a crucial role in health care. Counseling advice should therefore be evidence-based. Web-based information and databases are commonly used as electronic information sources. EVInews is a self-medication?related information tool consisting of a database and monthly published newsletters for pharmacists. Little is known about the quality of pharmacists? electronic information sources for evidence-based self-medication counseling. Objective: Our aim was to investigate the quality of community pharmacists? web-based search results for self-medication?related content in comparison with the EVInews database, based on an adjusted quality score for pharmacists. Methods: After receiving ethics approval, we performed a quantitative web-based survey with a search task as a prospective randomized, controlled, and unblinded trial. For the search task, participants were instructed to search for evidence-based information to verify 6 health-related statements from 2 typical self-medication indications. Pharmacists across Germany were invited via email to participate. After providing written informed consent, they were automatically, randomly assigned to use either web-based information sources of their choice without the EVInews database (web group) or exclusively the EVInews database (EVInews group). The quality of the information sources that were used for the search task was then assessed by 2 evaluators using a quality score ranging from 100% (180 points, all predefined criteria fulfilled) to 0% (0 points, none of the predefined criteria fulfilled). In case of assessment discrepancies, an expert panel consisting of 4 pharmacists was consulted. Results: In total, 141 pharmacists were enrolled. In the Web group (n=71 pharmacists), the median quality score was 32.8% (59.0 out of 180.0 points; IQR 23.0-80.5). In the EVInews group (n=70 pharmacists), the median quality score was significantly higher (85.3%; 153.5 out of 180.0 points; P<.001) and the IQR was smaller (IQR 125.1-157.0). Fewer pharmacists completed the entire search task in the Web group (n=22) than in the EVInews group (n=46). The median time to complete the search task was not significantly different between the Web group (25.4 minutes) and the EVInews group (19.7 minutes; P=.12). The most frequently used web-based sources (74/254, 29.1%) comprised tertiary literature. Conclusions: The median quality score of the web group was poor, and there was a significant difference in quality scores in favor of the EVInews group. Pharmacists? web-based and self-medication?related information sources often did not meet standard quality requirements and showed considerable variation in quality. Trial Registration: German Clinical Trials Register DRKS00026104; https://drks.de/search/en/trial/DRKS00026104 UR - https://www.jmir.org/2023/1/e45582 UR - http://dx.doi.org/10.2196/45582 UR - http://www.ncbi.nlm.nih.gov/pubmed/37342085 ID - info:doi/10.2196/45582 ER - TY - JOUR AU - Tang, Anne Leigh AU - Korona-Bailey, Jessica AU - Zaras, Dimitrios AU - Roberts, Allison AU - Mukhopadhyay, Sutapa AU - Espy, Stephen AU - Walsh, G. Colin PY - 2023/5/19 TI - Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study JO - JMIR Public Health Surveill SP - e45246 VL - 9 KW - fatal drug overdose KW - natural language processing KW - surveillance KW - Tennessee KW - State Unintentional Drug Overdose Reporting System KW - SUDORS N2 - Background: Fatal drug overdose surveillance informs prevention but is often delayed because of autopsy report processing and death certificate coding. Autopsy reports contain narrative text describing scene evidence and medical history (similar to preliminary death scene investigation reports) and may serve as early data sources for identifying fatal drug overdoses. To facilitate timely fatal overdose reporting, natural language processing was applied to narrative texts from autopsies. Objective: This study aimed to develop a natural language processing?based model that predicts the likelihood that an autopsy report narrative describes an accidental or undetermined fatal drug overdose. Methods: Autopsy reports of all manners of death (2019-2021) were obtained from the Tennessee Office of the State Chief Medical Examiner. The text was extracted from autopsy reports (PDFs) using optical character recognition. Three common narrative text sections were identified, concatenated, and preprocessed (bag-of-words) using term frequency?inverse document frequency scoring. Logistic regression, support vector machine (SVM), random forest, and gradient boosted tree classifiers were developed and validated. Models were trained and calibrated using autopsies from 2019 to 2020 and tested using those from 2021. Model discrimination was evaluated using the area under the receiver operating characteristic, precision, recall, F1-score, and F2-score (prioritizes recall over precision). Calibration was performed using logistic regression (Platt scaling) and evaluated using the Spiegelhalter z test. Shapley additive explanations values were generated for models compatible with this method. In a post hoc subgroup analysis of the random forest classifier, model discrimination was evaluated by forensic center, race, age, sex, and education level. Results: A total of 17,342 autopsies (n=5934, 34.22% cases) were used for model development and validation. The training set included 10,215 autopsies (n=3342, 32.72% cases), the calibration set included 538 autopsies (n=183, 34.01% cases), and the test set included 6589 autopsies (n=2409, 36.56% cases). The vocabulary set contained 4002 terms. All models showed excellent performance (area under the receiver operating characteristic ?0.95, precision ?0.94, recall ?0.92, F1-score ?0.94, and F2-score ?0.92). The SVM and random forest classifiers achieved the highest F2-scores (0.948 and 0.947, respectively). The logistic regression and random forest were calibrated (P=.95 and P=.85, respectively), whereas the SVM and gradient boosted tree classifiers were miscalibrated (P=.03 and P<.001, respectively). ?Fentanyl? and ?accident? had the highest Shapley additive explanations values. Post hoc subgroup analyses revealed lower F2-scores for autopsies from forensic centers D and E. Lower F2-score were observed for the American Indian, Asian, ?14 years, and ?65 years subgroups, but larger sample sizes are needed to validate these findings. Conclusions: The random forest classifier may be suitable for identifying potential accidental and undetermined fatal overdose autopsies. Further validation studies should be conducted to ensure early detection of accidental and undetermined fatal drug overdoses across all subgroups. UR - https://publichealth.jmir.org/2023/1/e45246 UR - http://dx.doi.org/10.2196/45246 UR - http://www.ncbi.nlm.nih.gov/pubmed/37204824 ID - info:doi/10.2196/45246 ER - TY - JOUR AU - Bota, Brianne A. AU - Bettinger, A. Julie AU - Sarfo-Mensah, Shirley AU - Lopez, Jimmy AU - Smith, P. David AU - Atkinson, M. Katherine AU - Bell, Cameron AU - Marty, Kim AU - Serhan, Mohamed AU - Zhu, T. David AU - McCarthy, E. Anne AU - Wilson, Kumanan PY - 2023/5/8 TI - Comparing the Use of a Mobile App and a Web-Based Notification Platform for Surveillance of Adverse Events Following Influenza Immunization: Randomized Controlled Trial JO - JMIR Public Health Surveill SP - e39700 VL - 9 KW - active participant?centered reporting KW - health technology KW - adverse event reporting KW - mobile apps KW - immunization KW - vaccine KW - safety KW - influenza KW - campaign KW - apps KW - mobile KW - surveillance KW - pharmacovigilance N2 - Background: Vaccine safety surveillance is a core component of vaccine pharmacovigilance. In Canada, active, participant-centered vaccine surveillance is available for influenza vaccines and has been used for COVID-19 vaccines. Objective: The objective of this study is to evaluate the effectiveness and feasibility of using a mobile app for reporting participant-centered seasonal influenza adverse events following immunization (AEFIs) compared to a web-based notification system. Methods: Participants were randomized to influenza vaccine safety reporting via a mobile app or a web-based notification platform. All participants were invited to complete a user experience survey. Results: Among the 2408 randomized participants, 1319 (54%) completed their safety survey 1 week after vaccination, with a higher completion rate among the web-based notification platform users (767/1196, 64%) than among mobile app users (552/1212, 45%; P<.001). Ease-of-use ratings were high for the web-based notification platform users (99% strongly agree or agree) and 88.8% of them strongly agreed or agreed that the system made reporting AEFIs easier. Web-based notification platform users supported the statement that a web-based notification-only approach would make it easier for public health professionals to detect vaccine safety signals (91.4%, agreed or strongly agreed). Conclusions: Participants in this study were significantly more likely to respond to a web-based safety survey rather than within a mobile app. These results suggest that mobile apps present an additional barrier for use compared to the web-based notification?only approach. Trial Registration: ClinicalTrials.gov NCT05794113; https://clinicaltrials.gov/show/NCT05794113 UR - https://publichealth.jmir.org/2023/1/e39700 UR - http://dx.doi.org/10.2196/39700 UR - http://www.ncbi.nlm.nih.gov/pubmed/37155240 ID - info:doi/10.2196/39700 ER - TY - JOUR AU - Karapetian, Karina AU - Jeon, Min Soo AU - Kwon, Jin-Won AU - Suh, Young-Kyoon PY - 2023/3/8 TI - Supervised Relation Extraction Between Suicide-Related Entities and Drugs: Development and Usability Study of an Annotated PubMed Corpus JO - J Med Internet Res SP - e41100 VL - 25 KW - suicide KW - adverse drug events KW - information extraction KW - relation classification KW - bidirectional encoder representations from transformers KW - pharmacovigilance KW - natural language processing KW - PubMed KW - corpus KW - language model N2 - Background: Drug-induced suicide has been debated as a crucial issue in both clinical and public health research. Published research articles contain valuable data on the drugs associated with suicidal adverse events. An automated process that extracts such information and rapidly detects drugs related to suicide risk is essential but has not been well established. Moreover, few data sets are available for training and validating classification models on drug-induced suicide. Objective: This study aimed to build a corpus of drug-suicide relations containing annotated entities for drugs, suicidal adverse events, and their relations. To confirm the effectiveness of the drug-suicide relation corpus, we evaluated the performance of a relation classification model using the corpus in conjunction with various embeddings. Methods: We collected the abstracts and titles of research articles associated with drugs and suicide from PubMed and manually annotated them along with their relations at the sentence level (adverse drug events, treatment, suicide means, or miscellaneous). To reduce the manual annotation effort, we preliminarily selected sentences with a pretrained zero-shot classifier or sentences containing only drug and suicide keywords. We trained a relation classification model using various Bidirectional Encoder Representations from Transformer embeddings with the proposed corpus. We then compared the performances of the model with different Bidirectional Encoder Representations from Transformer?based embeddings and selected the most suitable embedding for our corpus. Results: Our corpus comprised 11,894 sentences extracted from the titles and abstracts of the PubMed research articles. Each sentence was annotated with drug and suicide entities and the relationship between these 2 entities (adverse drug events, treatment, means, and miscellaneous). All of the tested relation classification models that were fine-tuned on the corpus accurately detected sentences of suicidal adverse events regardless of their pretrained type and data set properties. Conclusions: To our knowledge, this is the first and most extensive corpus of drug-suicide relations. UR - https://www.jmir.org/2023/1/e41100 UR - http://dx.doi.org/10.2196/41100 UR - http://www.ncbi.nlm.nih.gov/pubmed/36884281 ID - info:doi/10.2196/41100 ER - TY - JOUR AU - Fossouo Tagne, Joel AU - Yakob, Amin Reginald AU - Mcdonald, Rachael AU - Wickramasinghe, Nilmini PY - 2023/2/24 TI - Linking Activity Theory Within User-Centered Design: Novel Framework to Inform Design and Evaluation of Adverse Drug Reaction Reporting Systems in Pharmacy JO - JMIR Hum Factors SP - e43529 VL - 10 KW - pharmacovigilance KW - adverse drug reaction KW - pharmacist KW - user-centered design KW - activity theory N2 - Background: Adverse drug reactions (ADRs) may cause serious injuries including death. Timely reporting of ADRs may play a significant role in patient safety; however, underreporting exists. Enhancing the electronic communication of ADR information to regulators and between health care providers has the potential to reduce recurrent ADRs and improve patient safety. Objective: The main objectives were to explore the low rate of ADR reporting by community pharmacists (CPs) in Australia, evaluate the usability of an existing reporting system, and how this knowledge may influence the design of subsequent electronic ADR reporting systems. Methods: The study was carried out in 2 stages. Stage 1 involved qualitative semistructured interviews to identify CPs? perceived barriers and facilitators to ADR reporting. Data were analyzed by thematic analysis, and identified themes were subsequently aligned to the task-technology fit (TTF) framework. The second stage involved a usability evaluation of a commercial web-based ADR reporting system. A structured interview protocol that combined virtual observation, think-aloud moderating techniques, retrospective questioning of the overall user experience, and a System Usability Scale (SUS). The field notes from the interviews were subjected to thematic analysis. Results: In total, 12 CPs were interviewed in stage 1, and 7 CPs participated in stage 2. The interview findings show that CPs are willing to report ADRs but face barriers from environmental, organizational, and IT infrastructures. Increasing ADR awareness, improving workplace practices, and implementing user-focused electronic reporting systems were seen as facilitators of ADR reporting. User testing of an existing system resulted in above average usability (SUS 68.57); however, functional and user interpretation issues were identified. Design elements such as a drop-down menu, free-text entry, checkbox, and prefilled data fields were perceived to be extremely useful for navigating the system and facilitating ADR reporting. Conclusions: Existing reporting systems are not suited to report ADRs, or adapted to workflow, and are rarely used by CPs. Our study uncovered important contextual information for the design of future ADR reporting interventions. Based on our study, a multifaceted, theory-guided, user-centered, and best practice approach to design, implementation, and evaluation may be critical for the successful adoption of ADR reporting electronic interventions and patient safety. Future studies are needed to evaluate the effectiveness of theory-driven frameworks used in the design and implementation of ADR reporting systems. UR - https://humanfactors.jmir.org/2023/1/e43529 UR - http://dx.doi.org/10.2196/43529 UR - http://www.ncbi.nlm.nih.gov/pubmed/36826985 ID - info:doi/10.2196/43529 ER - TY - JOUR AU - Luderer, Hilary AU - Enman, Nicole AU - Gerwien, Robert AU - Braun, Stephen AU - McStocker, Samantha AU - Xiong, Xiaorui AU - Koebele, Carrington AU - Cannon, Christopher AU - Glass, Joseph AU - Maricich, Yuri PY - 2023/1/20 TI - A Prescription Digital Therapeutic to Support Unsupervised Buprenorphine Initiation for Patients With Opioid Use Disorder: Protocol for a Proof-of-Concept Study JO - JMIR Res Protoc SP - e43122 VL - 12 KW - buprenorphine KW - digital therapeutics KW - opioid use disorder KW - OUD KW - prescription digital therapeutic KW - PDT KW - reSET-O KW - unsupervised buprenorphine initiation KW - medication adherence KW - digital health intervention N2 - Background: Home-based (unsupervised) buprenorphine initiation is considered safe and effective, yet many patients report barriers to successful treatment initiation. Prescription digital therapeutics (PDTs) are software-based disease treatments regulated by the US Food and Drug Administration (FDA). The reSET-O PDT was authorized by the FDA in 2018 and delivers behavioral treatment for individuals receiving buprenorphine for opioid use disorder (OUD). A prototype PDT (PEAR-002b) designed for use with reSET-O was developed to assist in unsupervised buprenorphine initiation. Objective: The primary objective of this pilot study is to evaluate the acceptability of PEAR-002b in individuals with OUD who use it to support buprenorphine initiation, their unsupervised buprenorphine initiation success rate, and their medication adherence. Methods: Ten adults with OUD will be recruited for acceptability and feasibility testing. Outcomes will be assessed using week-1 visit attendance, participant interviews and satisfaction surveys, and urine drug screening (UDS). Three tools will be used in the study: PEAR-002b, reSET-O, and EmbracePlus. PEAR-002b includes a new set of features designed for use with reSET-O. The mechanism of action for the combined PEAR-002b and reSET-O treatment is a program of medication dosing support during week 1 of the initiation phase, cognitive behavioral therapy, and contingency management. During the medication initiation phase, participants are guided through a process to support proper medication use. PEAR-002b advises them when to take their buprenorphine based on provider inputs (eg, starting dose), self-reported substance use, and self-reported withdrawal symptoms. This study also administers the EmbracePlus device, a medical-grade smartwatch, to pilot methods for collecting physiologic data (eg, heart rate and skin conductance) and evaluate the device?s potential for use along with PDTs that are designed to improve OUD treatment initiation. Home buprenorphine initiation success will be summarized as the proportion of participants attending the post?buprenorphine initiation visit (week 1) and the proportion of participants who experience buprenorphine initiation?related adverse events (eg, precipitated withdrawal). Acceptability of PEAR-002b will be evaluated based on individual participants? ratings of ease of use, satisfaction, perceived helpfulness, and likelihood of recommending PEAR-002b. Medication adherence will be evaluated by participant self-report data and confirmed by UDS. UDS data will be summarized as the mean of individual participants? proportion of total urine samples testing positive for buprenorphine or norbuprenorphine over the 4-week study. Results: This project was funded in September 2019. As of September 2022, participant enrollment is ongoing. Conclusions: This is the first study to our knowledge to develop a PDT that assists with unsupervised buprenorphine initiation with the intent to better support patients and prescribers during this early phase of treatment. This pilot study will assess the acceptability and utility of a digital therapeutic to assist individuals with OUD with unsupervised buprenorphine initiation. Trial Registration: ClinicalTrials.gov NCT05412966; https://clinicaltrials.gov/ct2/show/NCT05412966 International Registered Report Identifier (IRRID): PRR1-10.2196/43122 UR - https://www.researchprotocols.org/2023/1/e43122 UR - http://dx.doi.org/10.2196/43122 UR - http://www.ncbi.nlm.nih.gov/pubmed/36662568 ID - info:doi/10.2196/43122 ER - TY - JOUR AU - Fossouo Tagne, Joel AU - Yakob, Amin Reginald AU - Dang, Ha Thu AU - Mcdonald, Rachael AU - Wickramasinghe, Nilmini PY - 2023/1/16 TI - Reporting, Monitoring, and Handling of Adverse Drug Reactions in Australia: Scoping Review JO - JMIR Public Health Surveill SP - e40080 VL - 9 KW - pharmacovigilance KW - adverse drug reactions KW - primary care KW - digital health N2 - Background: Adverse drug reactions (ADRs) are unintended consequences of medication use and may result in hospitalizations or deaths. Timely reporting of ADRs to regulators is essential for drug monitoring, research, and maintaining patient safety, but it has not been standardized in Australia. Objective: We sought to explore the ways that ADRs are monitored or reported in Australia. We reviewed how consumers and health care professionals participate in ADR monitoring and reporting. Methods: The Arksey and O?Malley framework provided a methodology to sort the data according to key themes and issues. Web of Science, Scopus, Embase, PubMed, CINAHL, and Computer & Applied Sciences Complete databases were used to extract articles published from 2010 to 2021. Two reviewers screened the papers for eligibility, extracted key data, and provided descriptive analysis of the data. Results: Seven articles met the inclusion criteria. The Adverse Medicine Events Line (telephone reporting service) was introduced in 2003 to support consumer reporting of ADRs; however, only 10.4% of consumers were aware of ADR reporting schemes. Consumers who experience side effects were more likely to report ADRs to their doctors or pharmacists than to the drug manufacturer. The documentation of ADR reports in hospital electronic health records showed that nurses and pharmacists were significantly less likely than doctors to omit the description of the drug reaction, and pharmacists were significantly more likely to enter the correct classification of the drug reaction than doctors. Review and analysis of all ADR reports submitted to the Therapeutic Goods Administration highlighted a decline in physician contribution from 28% of ADR reporting in 2003 to 4% in 2016; however, within this same time period, hospital and community pharmacists were a major source of ADR reporting (ie, 16%). In 2014, there was an increase in ADR reporting by community pharmacists following the introduction of the GuildLink ADR web-based reporting system; however, a year later, the reporting levels dropped. In 2018, the Therapeutic Goods Administration introduced a black triangle scheme on the packaging of newly approved medicines, to remind and encourage ADR reporting on new medicines, but this was only marginally successful at increasing the quantity of ADR reports. Conclusions: Despite the existence of national and international guidelines for ADR reporting and management, there is substantial interinstitutional variability in the standards of ADR reporting among individual health care facilities. There is room for increased ADR reporting rates among consumers and health care professionals. A thorough assessment of the barriers and enablers to ADR reporting at the primary health care institutional levels is essential. Interventions to increase ADR reporting, for example, the black triangle scheme (alert or awareness) or GuildLink (digital health), have only had marginal effects and may benefit from further improvement revisions and awareness programs. UR - https://publichealth.jmir.org/2023/1/e40080 UR - http://dx.doi.org/10.2196/40080 UR - http://www.ncbi.nlm.nih.gov/pubmed/36645706 ID - info:doi/10.2196/40080 ER - TY - JOUR AU - Dirkson, Anne AU - den Hollander, Dide AU - Verberne, Suzan AU - Desar, Ingrid AU - Husson, Olga AU - van der Graaf, A. Winette T. AU - Oosten, Astrid AU - Reyners, L. Anna K. AU - Steeghs, Neeltje AU - van Loon, Wouter AU - van Oortmerssen, Gerard AU - Gelderblom, Hans AU - Kraaij, Wessel PY - 2022/12/15 TI - Sample Bias in Web-Based Patient-Generated Health Data of Dutch Patients With Gastrointestinal Stromal Tumor: Survey Study JO - JMIR Form Res SP - e36755 VL - 6 IS - 12 KW - social media KW - patient forum KW - sample bias KW - representativeness KW - pharmacovigilance KW - rare cancer N2 - Background: Increasingly, social media is being recognized as a potential resource for patient-generated health data, for example, for pharmacovigilance. Although the representativeness of the web-based patient population is often noted as a concern, studies in this field are limited. Objective: This study aimed to investigate the sample bias of patient-centered social media in Dutch patients with gastrointestinal stromal tumor (GIST). Methods: A population-based survey was conducted in the Netherlands among 328 patients with GIST diagnosed 2-13 years ago to investigate their digital communication use with fellow patients. A logistic regression analysis was used to analyze clinical and demographic differences between forum users and nonusers. Results: Overall, 17.9% (59/328) of survey respondents reported having contact with fellow patients via social media. Moreover, 78% (46/59) of forum users made use of GIST patient forums. We found no statistically significant differences for age, sex, socioeconomic status, and time since diagnosis between forum users (n=46) and nonusers (n=273). Patient forum users did differ significantly in (self-reported) treatment phase from nonusers (P=.001). Of the 46 forum users, only 2 (4%) were cured and not being monitored; 3 (7%) were on adjuvant, curative treatment; 19 (41%) were being monitored after adjuvant treatment; and 22 (48%) were on palliative treatment. In contrast, of the 273 patients who did not use disease-specific forums to communicate with fellow patients, 56 (20.5%) were cured and not being monitored, 31 (11.3%) were on curative treatment, 139 (50.9%) were being monitored after treatment, and 42 (15.3%) were on palliative treatment. The odds of being on a patient forum were 2.8 times as high for a patient who is being monitored compared with a patient that is considered cured. The odds of being on a patient forum were 1.9 times as high for patients who were on curative (adjuvant) treatment and 10 times as high for patients who were in the palliative phase compared with patients who were considered cured. Forum users also reported a lower level of social functioning (84.8 out of 100) than nonusers (93.8 out of 100; P=.008). Conclusions: Forum users showed no particular bias on the most important demographic variables of age, sex, socioeconomic status, and time since diagnosis. This may reflect the narrowing digital divide. Overrepresentation and underrepresentation of patients with GIST in different treatment phases on social media should be taken into account when sourcing patient forums for patient-generated health data. A further investigation of the sample bias in other web-based patient populations is warranted. UR - https://formative.jmir.org/2022/12/e36755 UR - http://dx.doi.org/10.2196/36755 UR - http://www.ncbi.nlm.nih.gov/pubmed/36520526 ID - info:doi/10.2196/36755 ER - TY - JOUR AU - Zhou, ting Ting AU - Wang, Rui AU - Gu, jia Si AU - Xie, ling Li AU - Zhao, hua Qing AU - Xiao, zhao Ming AU - Chen, lu Yu PY - 2022/11/22 TI - Effectiveness of Mobile Medical Apps in Ensuring Medication Safety Among Patients With Chronic Diseases: Systematic Review and Meta-analysis JO - JMIR Mhealth Uhealth SP - e39819 VL - 10 IS - 11 KW - mobile application KW - medication safety KW - systematic review KW - meta-analysis KW - mobile health KW - mHealth KW - health app KW - adherence KW - pharmaceutical KW - drug safety KW - medication error KW - drug error KW - review methodology KW - search strategy KW - eHealth KW - digital health KW - adverse event KW - adverse effect N2 - Background: Along with the rapid growth of the global aging society, the mobile and health digital market has expanded greatly. Countless mobile medical apps (mmApps) have sprung up in the internet market, aiming to help patients with chronic diseases achieve medication safety. Objective: Based on the medication safety action plans proposed by the World Health Organization, we aimed to explore the effectiveness of mmApps in ensuring the medication safety of patients with chronic diseases, including whether mmApps can improve the willingness to report adverse drug events (ADEs), improve patients' medication adherence, and reduce medication errors. We hoped to verify our hypothesis through a systematic review and meta-analysis. Methods: The meta-analysis was performed in strict accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and included literature searched from 7 databases?PubMed, Web Of Science, Embase, CINAHL, China National Knowledge Infrastructure, Wanfang, and SinoMed. The publication time was limited to the time of database establishment to April 30, 2022. Studies were screened based on inclusion and exclusion criteria. The data extracted included authors, years of publication, countries or regions, participants? characteristics, intervention groups, and control groups, among others. Our quality assessment followed the guidelines of the Cochrane Handbook for Systematic Reviews of Interventions, Version 6.3. RevMan 5.2 software (Cochrane Collaboration) was used to analyze the statistical data, and a sensitivity analysis was performed to assess data stability. The degree of stability was calculated by using a different statistical method and excluding large-sample studies from the analysis. Results: We included 8 studies from 5 countries (China, the United States, France, Canada, and Spain) that were published from January 1, 2014, to December 31, 2021. The total number of participants was 1355, and we analyzed the characteristics of included studies, each app?s features, the risk of bias, and quality. The results showed that mmApps could increase ADE reporting willingness (relative risk [RR] 2.59, 95% CI 1.26-5.30; P=.009) and significantly improve medication adherence (RR 1.17, 95% CI 1.04-1.31; P=.007), but they had little effect on reducing medication errors (RR 1.54, 95% CI 0.33-7.29; P=.58). Conclusions: We analyzed the following three merits of mmApps, with regard to facilitating the willingness to report ADEs: mmApps facilitate more communication between patients and physicians, patients attach more importance to ADE reporting, and the processing of results is transparent. The use of mmApps improved medication adherence among patients with chronic diseases by conveying medical solutions, providing educational support, tracking medications, and allowing for remote consultations. Finally, we found 3 potential reasons for why our medication error results differed from those of other studies. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42022322072; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=322072 UR - https://mhealth.jmir.org/2022/11/e39819 UR - http://dx.doi.org/10.2196/39819 UR - http://www.ncbi.nlm.nih.gov/pubmed/36413386 ID - info:doi/10.2196/39819 ER - TY - JOUR AU - Gaspar, Frederic AU - Lutters, Monika AU - Beeler, Emanuel Patrick AU - Lang, Olivier Pierre AU - Burnand, Bernard AU - Rinaldi, Fabio AU - Lovis, Christian AU - Csajka, Chantal AU - Le Pogam, Marie-Annick AU - PY - 2022/11/15 TI - Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal JO - JMIR Res Protoc SP - e40456 VL - 11 IS - 11 KW - adverse drug events KW - adverse drug reactions KW - older inpatients KW - aged 65 and older KW - multimorbidity KW - polypharmacy KW - patient safety KW - inappropriate prescribing KW - medication errors KW - natural language processing KW - clinical decision support system KW - automated adverse drug event reporting system KW - electronic medical record KW - hospitals KW - multicenter study KW - interdisciplinary research KW - quality of hospital care KW - machine learning KW - antithrombotics KW - venous thromboembolism KW - hemorrhage N2 - Background: One-third of older inpatients experience adverse drug events (ADEs), which increase their mortality, morbidity, and health care use and costs. In particular, antithrombotic drugs are among the most at-risk medications for this population. Reporting systems have been implemented at the national, regional, and provider levels to monitor ADEs and design prevention strategies. Owing to their well-known limitations, automated detection technologies based on electronic medical records (EMRs) are being developed to routinely detect or predict ADEs. Objective: This study aims to develop and validate an automated detection tool for monitoring antithrombotic-related ADEs using EMRs from 4 large Swiss hospitals. We aim to assess cumulative incidences of hemorrhages and thromboses in older inpatients associated with the prescription of antithrombotic drugs, identify triggering factors, and propose improvements for clinical practice. Methods: This project is a multicenter, cross-sectional study based on 2015 to 2016 EMR data from 4 large hospitals in Switzerland: Lausanne, Geneva, and Zürich university hospitals, and Baden Cantonal Hospital. We have included inpatients aged ?65 years who stayed at 1 of the 4 hospitals during 2015 or 2016, received at least one antithrombotic drug during their stay, and signed or were not opposed to a general consent for participation in research. First, clinical experts selected a list of relevant antithrombotic drugs along with their side effects, risks, and confounding factors. Second, administrative, clinical, prescription, and laboratory data available in the form of free text and structured data were extracted from study participants? EMRs. Third, several automated rule-based and machine learning?based algorithms are being developed, allowing for the identification of hemorrhage and thromboembolic events and their triggering factors from the extracted information. Finally, we plan to validate the developed detection tools (one per ADE type) through manual medical record review. Performance metrics for assessing internal validity will comprise the area under the receiver operating characteristic curve, F1-score, sensitivity, specificity, and positive and negative predictive values. Results: After accounting for the inclusion and exclusion criteria, we will include 34,522 residents aged ?65 years. The data will be analyzed in 2022, and the research project will run until the end of 2022 to mid-2023. Conclusions: This project will allow for the introduction of measures to improve safety in prescribing antithrombotic drugs, which today remain among the drugs most involved in ADEs. The findings will be implemented in clinical practice using indicators of adverse events for risk management and training for health care professionals; the tools and methodologies developed will be disseminated for new research in this field. The increased performance of natural language processing as an important complement to structured data will bring existing tools to another level of efficiency in the detection of ADEs. Currently, such systems are unavailable in Switzerland. International Registered Report Identifier (IRRID): DERR1-10.2196/40456 UR - https://www.researchprotocols.org/2022/11/e40456 UR - http://dx.doi.org/10.2196/40456 UR - http://www.ncbi.nlm.nih.gov/pubmed/36378522 ID - info:doi/10.2196/40456 ER - TY - JOUR AU - Fossouo Tagne, Joel AU - Yakob, Amin Reginald AU - Mcdonald, Rachael AU - Wickramasinghe, Nilmini PY - 2022/10/11 TI - Barriers and Facilitators Influencing Real-time and Digital-Based Reporting of Adverse Drug Reactions by Community Pharmacists: Qualitative Study Using the Task-Technology Fit Framework JO - Interact J Med Res SP - e40597 VL - 11 IS - 2 KW - pharmacovigilance KW - adverse drug reaction KW - pharmacist KW - Task-Technology Fit KW - digital health N2 - Background: Medication use can result in adverse drug reactions (ADRs) that cause increased morbidity and health care consumption for patients and could potentially be fatal. Timely reporting of ADRs to regulators may contribute to patient safety by facilitating information gathering on drug safety data. Currently, little is known about how community pharmacists (CPs) monitor, handle, and report ADRs in Australia. Objective: This study aimed to identify perceived barriers to and facilitators of ADR reporting by CPs in Australia and suggest digital interventions. Methods: A qualitative study with individual interviews was conducted with CPs working across Victoria, Australia, between April 2022 and May 2022. A semistructured interview guide was used to identify perceived barriers to and facilitators of ADR reporting among CPs. The data were analyzed using thematic analysis. We constructed themes from the CP-reported barriers and facilitators. The themes were subsequently aligned with the Task-Technology Fit framework. Results: A total of 12 CPs were interviewed. Identified barriers were lack of knowledge of both the ADR reporting process and ADR reporting systems, time constraints, lack of financial incentives, lack of organizational support for ADR reporting, inadequate IT systems, and preference to refer consumers to physicians. The proposed facilitators of ADR reporting included enhancing CPs knowledge and awareness of ADRs, financial incentives for ADR reporting, workflow-integrated ADR reporting technology systems, feedback provision to CPs on the reported ADRs, and promoting consumer ADR reporting. Conclusions: Barriers to and facilitators of ADR reporting spanned both the task and technology aspects of the Task-Technology Fit model. Addressing the identified barriers to ADR reporting and providing workplace technologies that support ADR reporting may improve ADR reporting by CPs. Further investigations to observe ADR handling and reporting within community pharmacies can enhance patient safety by increasing ADR reporting by CPs. UR - https://www.i-jmr.org/2022/2/e40597 UR - http://dx.doi.org/10.2196/40597 UR - http://www.ncbi.nlm.nih.gov/pubmed/36222800 ID - info:doi/10.2196/40597 ER - TY - JOUR AU - Lim, Renly AU - Thornton, Christopher AU - Stanek, Jan AU - Ellett, Kalisch Lisa AU - Thiessen, Myra PY - 2022/10/7 TI - Development of a Web-Based System to Report Medication-Related Adverse Effects: Design and Usability Study JO - JMIR Form Res SP - e37605 VL - 6 IS - 10 KW - adverse drug reaction KW - adverse drug event KW - digital health KW - eHealth KW - medication safety KW - mHealth KW - participatory design KW - patient reported outcomes KW - telehealth N2 - Background: Medicine use is the most common intervention in health care. The frequency with which medicines are used means medication-related problems are very common. One common type of medication-related problems is adverse drug events, which are unintended and harmful effects associated with use of medicines. Reporting of adverse drug events to regulatory authorities is important for evaluation of safety of medicines; however, these adverse effects are frequently unreported due to various factors, including lack of consumer-friendly reporting tools. Objective: The aim of this study was to develop a user-friendly digital tool for consumers to report medication-related adverse effects. Methods: The project consisted of 3 parts: (1) content development, including a systematic literature search; (2) iterative system development; and (3) usability testing. The project was guided by participatory design principles, which suggest involving key stakeholders throughout the design process. The first 2 versions were developed as a mobile app and were tested with end users in 2 workshops. The third version was developed as a web application and was tested with consumers who were taking regular medicines. Consumers were asked to complete a modified version of the mHealth app usability questionnaire (MAUQ), an 18-item questionnaire with each item scored using a 7-point Likert scale ranging from 0 (strongly disagree) to 7 (strongly agree). The MAUQ assessed 3 subscales including ease of use (5 items), interface and satisfaction (7 items), and usefulness (6 items). Continuous variables were reported as mean (SD) values, whereas categorical variables were presented as frequencies (percentages). Data analysis was conducted in Microsoft Excel. Results: The content for the system was based on a systematic literature search and short-listing of questions, followed by feedback from project team members and consumers. Feedback from consumers in the 2 workshops were incorporated to improve the functionality, visual design, and stability of the third (current) version. The third version of the system was tested with 26 consumers. A total of 79% (N=307/390) of all responses on the MAUQ were scored 6 or 7, indicating that users generally strongly agree with the usability of the system. When looking at the individual domains, the system had an average score of 6.3 (SD 0.9) for ?ease of use,? 6.3 (SD 0.8) for ?interface and satisfaction,? and 5.2 (SD 1.4) for ?usefulness.? Conclusions: The web-based system for medicine adverse effects reporting is a user-friendly tool developed using an iterative participatory design approach. Future research includes further improving the system, particularly the usefulness of the system, as well as testing the scalability and performance of the system in practice. UR - https://formative.jmir.org/2022/10/e37605 UR - http://dx.doi.org/10.2196/37605 UR - http://www.ncbi.nlm.nih.gov/pubmed/36206034 ID - info:doi/10.2196/37605 ER - TY - JOUR AU - Lee, Suehyun AU - Lee, Hoon Jeong AU - Kim, Juyun Grace AU - Kim, Jong-Yeup AU - Shin, Hyunah AU - Ko, Inseok AU - Choe, Seon AU - Kim, Han Ju PY - 2022/10/6 TI - A Data-Driven Reference Standard for Adverse Drug Reaction (RS-ADR) Signal Assessment: Development and Validation JO - J Med Internet Res SP - e35464 VL - 24 IS - 10 KW - adverse drug reaction KW - ADR KW - real-world data KW - RWD KW - real-world evidence KW - RWE KW - pharmacovigilance KW - PV KW - reference standard KW - pharmacology KW - drug reaction N2 - Background: Pharmacovigilance using real-world data (RWD), such as multicenter electronic health records (EHRs), yields massively parallel adverse drug reaction (ADR) signals. However, proper validation of computationally detected ADR signals is not possible due to the lack of a reference standard for positive and negative associations. Objective: This study aimed to develop a reference standard for ADR (RS-ADR) to streamline the systematic detection, assessment, and understanding of almost all drug-ADR associations suggested by RWD analyses. Methods: We integrated well-known reference sets for drug-ADR pairs, including Side Effect Resource, Observational Medical Outcomes Partnership, and EU-ADR. We created a pharmacovigilance dictionary using controlled vocabularies and systematically annotated EHR data. Drug-ADR associations computed from MetaLAB and MetaNurse analyses of multicenter EHRs and extracted from the Food and Drug Administration Adverse Event Reporting System were integrated as ?empirically determined? positive and negative reference sets by means of cross-validation between institutions. Results: The RS-ADR consisted of 1344 drugs, 4485 ADRs, and 6,027,840 drug-ADR pairs with positive and negative consensus votes as pharmacovigilance reference sets. After the curation of the initial version of RS-ADR, novel ADR signals such as ?famotidine?hepatic function abnormal? were detected and reasonably validated by RS-ADR. Although the validation of the entire reference standard is challenging, especially with this initial version, the reference standard will improve as more RWD participate in the consensus voting with advanced pharmacovigilance dictionaries and analytic algorithms. One can check if a drug-ADR pair has been reported by our web-based search interface for RS-ADRs. Conclusions: RS-ADRs enriched with the pharmacovigilance dictionary, ADR knowledge, and real-world evidence from EHRs may streamline the systematic detection, evaluation, and causality assessment of computationally detected ADR signals. UR - https://www.jmir.org/2022/10/e35464 UR - http://dx.doi.org/10.2196/35464 UR - http://www.ncbi.nlm.nih.gov/pubmed/36201386 ID - info:doi/10.2196/35464 ER - TY - JOUR AU - Park, Hyunjung AU - Chae, Kathy Minjung AU - Jeong, Woohyeon AU - Yu, Jaeyong AU - Jung, Weon AU - Chang, Hansol AU - Cha, Chul Won PY - 2022/10/4 TI - Appropriateness of Alerts and Physicians? Responses With a Medication-Related Clinical Decision Support System: Retrospective Observational Study JO - JMIR Med Inform SP - e40511 VL - 10 IS - 10 KW - clinical decision support system KW - computerized physician order entry KW - alert fatigue KW - health personnel KW - decision-making support KW - physician behavior KW - physician response KW - alert system N2 - Background: Alert fatigue is unavoidable when many irrelevant alerts are generated in response to a small number of useful alerts. It is necessary to increase the effectiveness of the clinical decision support system (CDSS) by understanding physicians? responses. Objective: This study aimed to understand the CDSS and physicians? behavior by evaluating the clinical appropriateness of alerts and the corresponding physicians? responses in a medication-related passive alert system. Methods: Data on medication-related orders, alerts, and patients? electronic medical records were analyzed. The analyzed data were generated between August 2019 and June 2020 while the patient was in the emergency department. We evaluated the appropriateness of alerts and physicians? responses for a subset of 382 alert cases and classified them. Results: Of the 382 alert cases, only 7.3% (n=28) of the alerts were clinically appropriate. Regarding the appropriateness of the physicians? responses about the alerts, 92.4% (n=353) were deemed appropriate. In the classification of alerts, only 3.4% (n=13) of alerts were successfully triggered, and 2.1% (n=8) were inappropriate in both alert clinical relevance and physician?s response. In this study, the override rate was 92.9% (n=355). Conclusions: We evaluated the appropriateness of alerts and physicians? responses through a detailed medical record review of the medication-related passive alert system. An excessive number of unnecessary alerts are generated, because the algorithm operates as a rule base without reflecting the individual condition of the patient. It is important to maximize the value of the CDSS by comprehending physicians? responses. UR - https://medinform.jmir.org/2022/10/e40511 UR - http://dx.doi.org/10.2196/40511 UR - http://www.ncbi.nlm.nih.gov/pubmed/36194461 ID - info:doi/10.2196/40511 ER - TY - JOUR AU - Yu, Deahan AU - Vydiswaran, Vinod V. G. PY - 2022/9/28 TI - An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis JO - JMIR Med Inform SP - e38140 VL - 10 IS - 9 KW - natural language processing KW - machine learning KW - adverse drug event KW - pharmacovigilance KW - social media KW - drug KW - clinical KW - public health KW - health monitoring KW - surveillance KW - drug effects KW - drug safety N2 - Background: Adverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions. Objective: While using social media for collecting evidence of adverse drug events has potential, it is not clear whether social media are a reliable source for this information. Our work aims to (1) develop natural language processing approaches to identify adverse drug events on social media and (2) assess the reliability of social media data to identify adverse drug events. Methods: We propose a collocated long short-term memory network model with attentive pooling and aggregated, contextual representation generated by a pretrained model. We applied this model on large-scale Twitter data to identify adverse drug event?related tweets. We conducted a qualitative content analysis of these tweets to validate the reliability of social media data as a means to collect such information. Results: The model outperformed a variant without contextual representation during both the validation and evaluation phases. Through the content analysis of adverse drug event tweets, we observed that adverse drug event?related discussions had 7 themes. Mental health?related, sleep-related, and pain-related adverse drug event discussions were most frequent. We also contrast known adverse drug reactions to those mentioned in tweets. Conclusions: We observed a distinct improvement in the model when it used contextual information. However, our results reveal weak generalizability of the current systems to unseen data. Additional research is needed to fully utilize social media data and improve the robustness and reliability of natural language processing systems. The content analysis, on the other hand, showed that Twitter covered a sufficiently wide range of adverse drug events, as well as known adverse reactions, for the drugs mentioned in tweets. Our work demonstrates that social media can be a reliable data source for collecting adverse drug event mentions. UR - https://medinform.jmir.org/2022/9/e38140 UR - http://dx.doi.org/10.2196/38140 UR - http://www.ncbi.nlm.nih.gov/pubmed/36170004 ID - info:doi/10.2196/38140 ER - TY - JOUR AU - Venkatakrishnan, Ajit AU - Chu, Brandon AU - Aggarwal, Pushkar PY - 2022/8/10 TI - Photosensitivity From Avapritinib: Pharamacovigilance Analysis JO - JMIR Dermatol SP - e39229 VL - 5 IS - 3 KW - oncology KW - Avapritinib KW - drug-induced KW - adverse reaction KW - photosensitizer KW - photosensitizing KW - cancer KW - pharmacovigilance KW - pharmaceutical KW - photosensitive KW - photosensitivity KW - light KW - adverse event KW - side effect KW - tumor KW - pharmacology UR - https://derma.jmir.org/2022/3/e39229 UR - http://dx.doi.org/10.2196/39229 ID - info:doi/10.2196/39229 ER - TY - JOUR AU - Khademi Habibabadi, Sedigheh AU - Delir Haghighi, Pari AU - Burstein, Frada AU - Buttery, Jim PY - 2022/6/16 TI - Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study JO - JMIR Med Inform SP - e34305 VL - 10 IS - 6 KW - immunization KW - vaccines KW - natural language processing KW - vaccine adverse effects KW - vaccine safety KW - social media KW - Twitter KW - machine learning N2 - Background: Traditional monitoring for adverse events following immunization (AEFI) relies on various established reporting systems, where there is inevitable lag between an AEFI occurring and its potential reporting and subsequent processing of reports. AEFI safety signal detection strives to detect AEFI as early as possible, ideally close to real time. Monitoring social media data holds promise as a resource for this. Objective: The primary aim of this study is to investigate the utility of monitoring social media for gaining early insights into vaccine safety issues, by extracting vaccine adverse event mentions (VAEMs) from Twitter, using natural language processing techniques. The secondary aims are to document the natural language processing techniques used and identify the most effective of them for identifying tweets that contain VAEM, with a view to define an approach that might be applicable to other similar social media surveillance tasks. Methods: A VAEM-Mine method was developed that combines topic modeling with classification techniques to extract maximal VAEM posts from a vaccine-related Twitter stream, with high degree of confidence. The approach does not require a targeted search for specific vaccine reaction?indicative words, but instead, identifies VAEM posts according to their language structure. Results: The VAEM-Mine method isolated 8992 VAEMs from 811,010 vaccine-related Twitter posts and achieved an F1 score of 0.91 in the classification phase. Conclusions: Social media can assist with the detection of vaccine safety signals as a valuable complementary source for monitoring mentions of vaccine adverse events. A social media?based VAEM data stream can be assessed for changes to detect possible emerging vaccine safety signals, helping to address the well-recognized limitations of passive reporting systems, including lack of timeliness and underreporting. UR - https://medinform.jmir.org/2022/6/e34305 UR - http://dx.doi.org/10.2196/34305 UR - http://www.ncbi.nlm.nih.gov/pubmed/35708760 ID - info:doi/10.2196/34305 ER - TY - JOUR AU - Zheng, Chengyi AU - Duffy, Jonathan AU - Liu, Amy In-Lu AU - Sy, S. Lina AU - Navarro, A. Ronald AU - Kim, S. Sunhea AU - Ryan, S. Denison AU - Chen, Wansu AU - Qian, Lei AU - Mercado, Cheryl AU - Jacobsen, J. Steven PY - 2022/5/24 TI - Identifying Cases of Shoulder Injury Related to Vaccine Administration (SIRVA) in the United States: Development and Validation of a Natural Language Processing Method JO - JMIR Public Health Surveill SP - e30426 VL - 8 IS - 5 KW - health KW - informatics KW - shoulder injury related to vaccine administration KW - SIRVA KW - natural language processing KW - NLP KW - causal relation KW - temporal relation KW - pharmacovigilance KW - electronic health records KW - EHR KW - vaccine safety KW - artificial intelligence KW - big data KW - population health KW - real-world data KW - vaccines N2 - Background: Shoulder injury related to vaccine administration (SIRVA) accounts for more than half of all claims received by the National Vaccine Injury Compensation Program. However, due to the difficulty of finding SIRVA cases in large health care databases, population-based studies are scarce. Objective: The goal of the research was to develop a natural language processing (NLP) method to identify SIRVA cases from clinical notes. Methods: We conducted the study among members of a large integrated health care organization who were vaccinated between April 1, 2016, and December 31, 2017, and had subsequent diagnosis codes indicative of shoulder injury. Based on a training data set with a chart review reference standard of 164 cases, we developed an NLP algorithm to extract shoulder disorder information, including prior vaccination, anatomic location, temporality and causality. The algorithm identified 3 groups of positive SIRVA cases (definite, probable, and possible) based on the strength of evidence. We compared NLP results to a chart review reference standard of 100 vaccinated cases. We then applied the final automated NLP algorithm to a broader cohort of vaccinated persons with a shoulder injury diagnosis code and performed manual chart confirmation on a random sample of NLP-identified definite cases and all NLP-identified probable and possible cases. Results: In the validation sample, the NLP algorithm had 100% accuracy for identifying 4 SIRVA cases and 96 cases without SIRVA. In the broader cohort of 53,585 vaccinations, the NLP algorithm identified 291 definite, 124 probable, and 52 possible SIRVA cases. The chart-confirmation rates for these groups were 95.5% (278/291), 67.7% (84/124), and 17.3% (9/52), respectively. Conclusions: The algorithm performed with high sensitivity and reasonable specificity in identifying positive SIRVA cases. The NLP algorithm can potentially be used in future population-based studies to identify this rare adverse event, avoiding labor-intensive chart review validation. UR - https://publichealth.jmir.org/2022/5/e30426 UR - http://dx.doi.org/10.2196/30426 UR - http://www.ncbi.nlm.nih.gov/pubmed/35608886 ID - info:doi/10.2196/30426 ER - TY - JOUR AU - Portelli, Beatrice AU - Scaboro, Simone AU - Tonino, Roberto AU - Chersoni, Emmanuele AU - Santus, Enrico AU - Serra, Giuseppe PY - 2022/5/13 TI - Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets JO - J Med Internet Res SP - e35115 VL - 24 IS - 5 KW - adverse drug events KW - COVID-19 KW - digital pharmacovigilance KW - opinion mining KW - vaccines KW - social media KW - machine learning KW - deep learning KW - learning models KW - sentiment analysis KW - Twitter analysis KW - Twitter KW - web portal KW - public health N2 - Background: In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments. Objective: Monitoring social media for the early identification of side effects, and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective of this study was to create a web portal to monitor the opinion of social media users on COVID-19 vaccines, which can offer a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign. Methods: We developed a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among other techniques, a state-of-the-art system for the identification of adverse drug events on social media; natural language processing models for sentiment analysis; statistical tools; and open-source databases to visualize the trending hashtags, news articles, and their factuality. All modules of the system are displayed through an open web portal. Results: A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot?related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. Conclusions: We present a tool connected with a web portal to monitor and display some key aspects of the public?s reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model. UR - https://www.jmir.org/2022/5/e35115 UR - http://dx.doi.org/10.2196/35115 UR - http://www.ncbi.nlm.nih.gov/pubmed/35446781 ID - info:doi/10.2196/35115 ER - TY - JOUR AU - Garcia, Cristian AU - Rehman, Nadia AU - Lawson, O. Daeria AU - Djiadeu, Pascal AU - Mbuagbaw, Lawrence PY - 2022/5/13 TI - Developing Reporting Guidelines for Studies of HIV Drug Resistance Prevalence: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e35969 VL - 11 IS - 5 KW - HIV KW - drug resistance KW - reporting guideline KW - prevalence KW - surveillance KW - antiretroviral therapy KW - report KW - global health KW - problem N2 - Background: HIV drug resistance is a global health problem that limits the effectiveness of antiretroviral therapy. Adequate surveillance of HIV drug resistance is challenged by heterogenous and inadequate data reporting, which compromises the accuracy, interpretation, and usability of prevalence estimates. Previous research has found that the quality of reporting in studies of HIV drug resistance prevalence is low, and thus better guidance is needed to ensure complete and uniform reporting. Objective: This paper contributes to the process of developing reporting guidelines for prevalence studies of HIV drug resistance by reporting the methodology used in creating a reporting item checklist and generating key insights on items that are important to report. Methods: We will conduct a sequential explanatory mixed methods study among authors and users of studies of HIV drug resistance. The two-phase design will include a cross-sectional electronic survey (quantitative phase) followed by a focus group discussion (qualitative phase). Survey participants will rate the essentiality of various reporting items. This data will be analyzed using content validity ratios to determine the items that will be retained for focus group discussions. Participants in these discussions will revise the items and any additionally suggested items and settle on a complete reporting item checklist. We will also conduct a thematic analysis of the group discussions to identify emergent themes regarding the agreement process. Results: As of November 2021, data collection for both phases of the study is complete. In July 2021, 51 participants had provided informed consent and completed the electronic survey. In October 2021, focus group discussions were held. Nine participants in total participated in two virtual focus group discussions. As of May 2022, data are being analyzed. Conclusions: This study supports the development of a reporting checklist for studies of HIV drug resistance by achieving agreement among experts on what items should be reported in these studies. The results of this work will be refined and elaborated on by a writing committee of HIV drug resistance experts and external reviewers to develop finalized reporting guidelines. International Registered Report Identifier (IRRID): DERR1-10.2196/35969 UR - https://www.researchprotocols.org/2022/5/e35969 UR - http://dx.doi.org/10.2196/35969 UR - http://www.ncbi.nlm.nih.gov/pubmed/35559984 ID - info:doi/10.2196/35969 ER - TY - JOUR AU - Tagwerker, Christian AU - Carias-Marines, Jane Mary AU - Smith, J. David PY - 2022/5/3 TI - Effects of Pharmacogenomic Testing in Clinical Pain Management: Retrospective Study JO - JMIRx Med SP - e32902 VL - 3 IS - 2 KW - pharmacogenomics KW - pain management KW - drug-drug interaction KW - DDI KW - pharmacy KW - prescriptions KW - genetics KW - genomics KW - drug-gene interaction KW - pain N2 - Background: The availability of pharmacogenomic (PGx) methods to determine the right drug and dosage for individualized patient treatment has increased over the past decade. Adoption of the resulting PGx reports in a clinical setting and monitoring of clinical outcomes is a challenging and long-term commitment. Objective: This study summarizes an extended PGx deep sequencing panel intended for medication dosing and prescription guidance newly adopted in a pain management clinic. The primary outcome of this retrospective study reports the number of cases and types of drugs covered, for which PGx data appears to have assisted in optimal drug prescription and dosing. Methods: A PGx panel is described, encompassing 23 genes and 141 single-nucleotide polymorphisms or indels, combined with PGx dosing guidance and drug-gene interaction (DGI) and drug-drug interaction (DDI) reporting to prevent adverse drug reactions (ADRs). During a 2-year period, patients (N=171) were monitored in a pain management clinic. Urine toxicology, PGx reports, and progress notes were studied retrospectively for changes in prescription regimens before and after the PGx report was made available to the provider. An additional algorithm provided DGIs and DDIs to prevent ADRs. Results: Among patient PGx reports with medication lists provided (n=146), 57.5% (n=84) showed one or more moderate and 5.5% (n=8) at least one serious PGx interaction. A total of 96 (65.8%) patients showed at least one moderate and 15.1% (n=22) one or more serious DGIs or DDIs. A significant number of active changes in prescriptions based on the 102 PGx/DGI/DDI report results provided was observed for 85 (83.3%) patients for which a specific drug was either discontinued or switched within the defined drug classes of the report, or a new drug was added. Conclusions: Preventative action was observed for all serious interactions, and only moderate interactions were tolerated for the lack of other alternatives. This study demonstrates the application of an extended PGx panel combined with a customized informational report to prevent ADRs and improve patient care. UR - https://med.jmirx.org/2022/2/e32902 UR - http://dx.doi.org/10.2196/32902 UR - http://www.ncbi.nlm.nih.gov/pubmed/37725552 ID - info:doi/10.2196/32902 ER - TY - JOUR AU - Abell-Hart, Kayley AU - Rashidian, Sina AU - Teng, Dejun AU - Rosenthal, N. Richard AU - Wang, Fusheng PY - 2022/4/12 TI - Where Opioid Overdose Patients Live Far From Treatment: Geospatial Analysis of Underserved Populations in New York State JO - JMIR Public Health Surveill SP - e32133 VL - 8 IS - 4 KW - opioid use disorder KW - opioid overdose KW - buprenorphine KW - naloxone KW - geospatial analysis KW - epidemiology KW - opioid pandemic KW - public health N2 - Background: Opioid addiction and overdose have a large burden of disease and mortality in New York State (NYS). The medication naloxone can reverse an overdose, and buprenorphine can treat opioid use disorder. Efforts to increase the accessibility of both medications include a naloxone standing order and a waiver program for prescribing buprenorphine outside a licensed drug treatment program. However, only a slim majority of NYS pharmacies are listed as participating in the naloxone standing order, and less than 7% of prescribers in NYS have a buprenorphine waiver. Therefore, there is a significant opportunity to increase access. Objective: Identifying the geographic regions of NYS that are farthest from resources can help target interventions to improve access to naloxone and buprenorphine. To maximize the efficiency of such efforts, we also sought to determine where these underserved regions overlap with the largest numbers of actual patients who have experienced opioid overdose. Methods: We used address data to assess the spatial distribution of naloxone pharmacies and buprenorphine prescribers. Using the home addresses of patients who had an opioid overdose, we identified geographic locations of resource deficits. We report findings at the high spatial granularity of census tracts, with some neighboring census tracts merged to preserve privacy. Results: We identified several hot spots, where many patients live far from the nearest resource of each type. The highest density of patients in areas far from naloxone pharmacies was found in eastern Broome county. For areas far from buprenorphine prescribers, we identified subregions of Oswego county and Wayne county as having a high number of potentially underserved patients. Conclusions: Although NYS is home to thousands of naloxone pharmacies and potential buprenorphine prescribers, access is not uniform. Spatial analysis revealed census tract areas that are far from resources, yet contain the residences of many patients who have experienced opioid overdose. Our findings have implications for public health decision support in NYS. Our methods for privacy can also be applied to other spatial supply-demand problems involving sensitive data. UR - https://publichealth.jmir.org/2022/4/e32133 UR - http://dx.doi.org/10.2196/32133 UR - http://www.ncbi.nlm.nih.gov/pubmed/35412467 ID - info:doi/10.2196/32133 ER - TY - JOUR AU - Lerner, Ivan AU - Serret-Larmande, Arnaud AU - Rance, Bastien AU - Garcelon, Nicolas AU - Burgun, Anita AU - Chouchana, Laurent AU - Neuraz, Antoine PY - 2022/3/30 TI - Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS) JO - JMIR Med Inform SP - e35190 VL - 10 IS - 3 KW - COVID-19 KW - drug repurposing KW - wide association studies KW - clinical data KW - pharmacopeia KW - electronic medical records KW - health data KW - mortality rate KW - hospitalization KW - patient data N2 - Background: Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. Objective: With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. Methods: We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. Results: A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.01) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively. Conclusions: Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed. UR - https://medinform.jmir.org/2022/3/e35190 UR - http://dx.doi.org/10.2196/35190 UR - http://www.ncbi.nlm.nih.gov/pubmed/35275837 ID - info:doi/10.2196/35190 ER - TY - JOUR AU - de Lusignan, Simon AU - Tsang, M. Ruby S. AU - Akinyemi, Oluwafunmi AU - Lopez Bernal, Jamie AU - Amirthalingam, Gayatri AU - Sherlock, Julian AU - Smith, Gillian AU - Zambon, Maria AU - Howsam, Gary AU - Joy, Mark PY - 2022/3/28 TI - Adverse Events of Interest Following Influenza Vaccination in the First Season of Adjuvanted Trivalent Immunization: Retrospective Cohort Study JO - JMIR Public Health Surveill SP - e25803 VL - 8 IS - 3 KW - influenza KW - influenza vaccines KW - adverse events of interest KW - computerized medical record systems KW - sentinel surveillance N2 - Background: Vaccination is the most effective form of prevention of seasonal influenza; the United Kingdom has a national influenza vaccination program to cover targeted population groups. Influenza vaccines are known to be associated with some common minor adverse events of interest (AEIs), but it is not known if the adjuvanted trivalent influenza vaccine (aTIV), first offered in the 2018/2019 season, would be associated with more AEIs than other types of vaccines. Objective: We aim to compare the incidence of AEIs associated with different types of seasonal influenza vaccines offered in the 2018/2019 season. Methods: We carried out a retrospective cohort study using computerized medical record data from the Royal College of General Practitioners Research and Surveillance Centre sentinel network database. We extracted data on vaccine exposure and consultations for European Medicines Agency?specified AEIs for the 2018/2019 influenza season. We used a self-controlled case series design; computed relative incidence (RI) of AEIs following vaccination; and compared the incidence of AEIs associated with aTIV, the quadrivalent influenza vaccine, and the live attenuated influenza vaccine. We also compared the incidence of AEIs for vaccinations that took place in a practice with those that took place elsewhere. Results: A total of 1,024,160 individuals received a seasonal influenza vaccine, of which 165,723 individuals reported a total of 283,355 compatible symptoms in the 2018/2019 season. Most AEIs occurred within 7 days following vaccination, with a seasonal effect observed. Using aTIV as the reference group, the quadrivalent influenza vaccine was associated with a higher incidence of AEIs (RI 1.46, 95% CI 1.41-1.52), whereas the live attenuated influenza vaccine was associated with a lower incidence of AEIs (RI 0.79, 95% CI 0.73-0.83). No effect of vaccination setting on the incidence of AEIs was observed. Conclusions: Routine sentinel network data offer an opportunity to make comparisons between safety profiles of different vaccines. Evidence that supports the safety of newer types of vaccines may be reassuring for patients and could help improve uptake in the future. UR - https://publichealth.jmir.org/2022/3/e25803 UR - http://dx.doi.org/10.2196/25803 UR - http://www.ncbi.nlm.nih.gov/pubmed/35343907 ID - info:doi/10.2196/25803 ER - TY - JOUR AU - Zusman, Z. Enav AU - Lavu, Alekhya AU - Pawliuk, Colleen AU - Pawluski, Jodi AU - Hutchison, M. Sarah AU - Platt, W. Robert AU - Oberlander, F. Tim PY - 2022/3/28 TI - Associations Between Prenatal Exposure to Serotonergic Medications and Biobehavioral Stress Regulation: Protocol for a Systematic Review and Meta-analysis JO - JMIR Res Protoc SP - e33363 VL - 11 IS - 3 KW - pregnancy KW - serotonergic medications KW - antidepressants KW - stress regulation KW - systematic review KW - meta-analysis N2 - Background: Up to 20% of mothers experience antenatal depression and approximately 30% of these women are treated with serotonergic psychotropic pharmacological therapy during pregnancy. Serotonergic antidepressants readily cross the placenta and the fetal blood-brain barrier, altering central synaptic serotonin signaling and potentially altering serotonin levels in the developing fetal brain. Objective: The aim of this study is to assess the impact of prenatal exposure to serotonergic antidepressants, accounting for maternal mood disturbances, on markers of stress regulation during childhood. Methods: We will follow PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and will search MEDLINE, Embase, CINAHL, PsycINFO, and ClinicalTrials.gov for full-length studies that assessed physiological (eg, cortisol level, heart rate variability, salivary amylase, pupillary size, C-reactive protein) indices of stress regulation in children of pregnant people who were treated with a serotonergic antidepressant at any point during pregnancy. We will assess the quality of observational studies using the Newcastle-Ottawa Scale and the quality of experimental studies using the Cochrane risk-of-bias tool. When possible, we will conduct a random-effects meta-analysis. If meta-analysis is not possible, we will conduct a narrative review. If a sufficient number of studies are found, we will perform subgroup analysis and assess outcomes measured by drug class, dose, trimester of exposure, and child?s age and gender. Results: We registered our review protocol with PROSPERO (International Prospective Register of Systematic Reviews; CRD42021275750), completed the literature search, and initiated title and abstract review in August 2021. We expect to finalize this review by April 2022. Conclusions: Findings should identify the impact of prenatal antidepressant effects on stress regulation and distinguish it from the impact of prenatal exposure to maternal mood disturbances. This review should inform decisions about serotonergic antidepressant use during pregnancy. Trial Registration: PROSPERO CRD42021275750; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=275750 International Registered Report Identifier (IRRID): PRR1-10.2196/33363 UR - https://www.researchprotocols.org/2022/3/e33363 UR - http://dx.doi.org/10.2196/33363 UR - http://www.ncbi.nlm.nih.gov/pubmed/35343913 ID - info:doi/10.2196/33363 ER - TY - JOUR AU - Hek, Karin AU - Rolfes, Leàn AU - van Puijenbroek, P. Eugène AU - Flinterman, E. Linda AU - Vorstenbosch, Saskia AU - van Dijk, Liset AU - Verheij, A. Robert PY - 2022/3/16 TI - Electronic Health Record?Triggered Research Infrastructure Combining Real-world Electronic Health Record Data and Patient-Reported Outcomes to Detect Benefits, Risks, and Impact of Medication: Development Study JO - JMIR Med Inform SP - e33250 VL - 10 IS - 3 KW - adverse drug reaction KW - general practice KW - patient-reported outcome KW - electronic health record KW - overactive bladder KW - research infrastructure KW - learning health systems N2 - Background: Real-world data from electronic health records (EHRs) represent a wealth of information for studying the benefits and risks of medical treatment. However, they are limited in scope and should be complemented by information from the patient perspective. Objective: The aim of this study is to develop an innovative research infrastructure that combines information from EHRs with patient experiences reported in questionnaires to monitor the risks and benefits of medical treatment. Methods: We focused on the treatment of overactive bladder (OAB) in general practice as a use case. To develop the Benefit, Risk, and Impact of Medication Monitor (BRIMM) infrastructure, we first performed a requirement analysis. BRIMM?s starting point is routinely recorded general practice EHR data that are sent to the Dutch Nivel Primary Care Database weekly. Patients with OAB were flagged weekly on the basis of diagnoses and prescriptions. They were invited subsequently for participation by their general practitioner (GP), via a trusted third party. Patients received a series of questionnaires on disease status, pharmacological and nonpharmacological treatments, adverse drug reactions, drug adherence, and quality of life. The questionnaires and a dedicated feedback portal were developed in collaboration with a patient association for pelvic-related diseases, Bekkenbodem4All. Participating patients and GPs received feedback. An expert meeting was organized to assess the strengths, weaknesses, opportunities, and threats of the new research infrastructure. Results: The BRIMM infrastructure was developed and implemented. In the Nivel Primary Care Database, 2933 patients with OAB from 27 general practices were flagged. GPs selected 1636 (55.78%) patients who were eligible for the study, of whom 295 (18.0% of eligible patients) completed the first questionnaire. A total of 288 (97.6%) patients consented to the linkage of their questionnaire data with their EHR data. According to experts, the strengths of the infrastructure were the linkage of patient-reported outcomes with EHR data, comparison of pharmacological and nonpharmacological treatments, flexibility of the infrastructure, and low registration burden for GPs. Methodological weaknesses, such as susceptibility to bias, patient selection, and low participation rates among GPs and patients, were seen as weaknesses and threats. Opportunities represent usefulness for policy makers and health professionals, conditional approval of medication, data linkage to other data sources, and feedback to patients. Conclusions: The BRIMM research infrastructure has the potential to assess the benefits and safety of (medical) treatment in real-life situations using a unique combination of EHRs and patient-reported outcomes. As patient involvement is an important aspect of the treatment process, generating knowledge from clinical and patient perspectives is valuable for health care providers, patients, and policy makers. The developed methodology can easily be applied to other treatments and health problems. UR - https://medinform.jmir.org/2022/3/e33250 UR - http://dx.doi.org/10.2196/33250 UR - http://www.ncbi.nlm.nih.gov/pubmed/35293877 ID - info:doi/10.2196/33250 ER - TY - JOUR AU - Ozawa, Sachiko AU - Billings, Joanna AU - Sun, Yujiao AU - Yu, Sushan AU - Penley, Benjamin PY - 2022/2/16 TI - COVID-19 Treatments Sold Online Without Prescription Requirements in the United States: Cross-sectional Study Evaluating Availability, Safety and Marketing of Medications JO - J Med Internet Res SP - e27704 VL - 24 IS - 2 KW - COVID-19 KW - medication KW - internet KW - online pharmacy KW - drug N2 - Background: The COVID-19 pandemic has increased online purchases and heightened interest in existing treatments. Dexamethasone, hydroxychloroquine, and lopinavir-ritonavir have been touted as potential COVID-19 treatments. Objective: This study assessed the availability of 3 potential COVID-19 treatments online and evaluated the safety and marketing characteristics of websites selling these products during the pandemic. Methods: A cross-sectional study was conducted in the months of June 2020 to August 2020, by searching the first 100 results on Google, Bing, and Yahoo! mimicking a US consumer. Unique websites were included if they sold targeted medicines, were in English, offered US shipping, and were free to access. Identified online pharmacies were categorized as rogue, unclassified, or legitimate based on LegitScript classifications. Patient safety characteristics, marketing techniques, price, legitimacy, IP addresses, and COVID-19 mentions were recorded. Results: We found 117 websites: 30 selling dexamethasone (19/30, 63% rogue), 39 selling hydroxychloroquine (22/39, 56% rogue), and 48 selling lopinavir-ritonavir (33/48, 69% rogue). This included 89 unique online pharmacies: 70% were rogue (n=62), 22% were unapproved (n=20), and 8% were considered legitimate (n=7). Prescriptions were not required among 100% (19/19), 61% (20/33), and 50% (11/22) of rogue websites selling dexamethasone, lopinavir-ritonavir, and hydroxychloroquine, respectively. Overall, only 32% (24/74) of rogue websites required prescriptions to buy these medications compared with 94% (31/33) of unapproved and 100% (10/10) of legitimate websites (P<.001). Rogue sites rarely offered pharmacist counseling (1/33, 3% for lopinavir-ritonavir to 2/22, 9% for hydroxychloroquine). Drug warnings were unavailable in 86% (6/7) of unapproved dexamethasone sites. It was difficult to distinguish between rogue, unapproved, and legitimate online pharmacies solely based on website marketing characteristics. Illegitimate pharmacies were more likely to offer bulk discounts and claim price discounts, yet dexamethasone and hydroxychloroquine were more expensive online. An inexpensive generic version of lopinavir-ritonavir that is not authorized for use in the United States was available online offering US shipping. Some websites claimed hydroxychloroquine and lopinavir-ritonavir were effective COVID-19 treatments despite lack of scientific evidence. In comparing IP addresses to locations claimed on the websites, only 8.5% (7/82) matched their claimed locations. Conclusions: The lack of safety measures by illegitimate online pharmacies endanger patients, facilitating access to medications without appropriate oversight by health care providers to monitor clinical response, drug interactions, and adverse effects. We demonstrated how easy it is to go online to buy medications that are touted to treat COVID-19 even when current clinical evidence does not support their use for self-treatment. We documented that illegitimate online pharmacies sidestep prescription requirements, skirt pharmacist counseling, and make false claims regarding efficacy for COVID-19 treatment. Health care professionals must urgently educate the public of the dangers of purchasing drugs from illegitimate websites and highlight the importance of seeking treatment through authentic avenues of care. UR - https://www.jmir.org/2022/2/e27704 UR - http://dx.doi.org/10.2196/27704 UR - http://www.ncbi.nlm.nih.gov/pubmed/34662286 ID - info:doi/10.2196/27704 ER - TY - JOUR AU - Facile, Rhonda AU - Muhlbradt, Elizabeth Erin AU - Gong, Mengchun AU - Li, Qingna AU - Popat, Vaishali AU - Pétavy, Frank AU - Cornet, Ronald AU - Ruan, Yaoping AU - Koide, Daisuke AU - Saito, I. Toshiki AU - Hume, Sam AU - Rockhold, Frank AU - Bao, Wenjun AU - Dubman, Sue AU - Jauregui Wurst, Barbara PY - 2022/1/27 TI - Use of Clinical Data Interchange Standards Consortium (CDISC) Standards for Real-world Data: Expert Perspectives From a Qualitative Delphi Survey JO - JMIR Med Inform SP - e30363 VL - 10 IS - 1 KW - real-world data KW - real-world evidence KW - clinical trials KW - Delphi survey KW - clinical data standards KW - regulatory submission KW - academic research KW - public health data KW - registry data KW - electronic health records KW - observational data KW - data integration KW - FAIR principles N2 - Background: Real-world data (RWD) and real-world evidence (RWE) are playing increasingly important roles in clinical research and health care decision-making. To leverage RWD and generate reliable RWE, data should be well defined and structured in a way that is semantically interoperable and consistent across stakeholders. The adoption of data standards is one of the cornerstones supporting high-quality evidence for the development of clinical medicine and therapeutics. Clinical Data Interchange Standards Consortium (CDISC) data standards are mature, globally recognized, and heavily used by the pharmaceutical industry for regulatory submissions. The CDISC RWD Connect Initiative aims to better understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance needed to more easily implement them. Objective: The aim of this study is to understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance that may be needed to implement CDISC standards more easily for this purpose. Methods: We conducted a qualitative Delphi survey involving an expert advisory board with multiple key stakeholders, with 3 rounds of input and review. Results: Overall, 66 experts participated in round 1, 56 in round 2, and 49 in round 3 of the Delphi survey. Their inputs were collected and analyzed, culminating in group statements. It was widely agreed that the standardization of RWD is highly necessary, and the primary focus should be on its ability to improve data sharing and the quality of RWE. The priorities for RWD standardization included electronic health records, such as data shared using Health Level 7 Fast Health care Interoperability Resources (FHIR), and the data stemming from observational studies. With different standardization efforts already underway in these areas, a gap analysis should be performed to identify the areas where synergies and efficiencies are possible and then collaborate with stakeholders to create or extend existing mappings between CDISC and other standards, controlled terminologies, and models to represent data originating across different sources. Conclusions: There are many ongoing data standardization efforts around human health data?related activities, each with different definitions, levels of granularity, and purpose. Among these, CDISC has been successful in standardizing clinical trial-based data for regulation worldwide. However, the complexity of the CDISC standards and the fact that they were developed for different purposes, combined with the lack of awareness and incentives to use a new standard and insufficient training and implementation support, are significant barriers to setting up the use of CDISC standards for RWD. The collection and dissemination of use cases, development of tools and support systems for the RWD community, and collaboration with other standards development organizations are potential steps forward. Using CDISC will help link clinical trial data and RWD and promote innovation in health data science. UR - https://medinform.jmir.org/2022/1/e30363 UR - http://dx.doi.org/10.2196/30363 UR - http://www.ncbi.nlm.nih.gov/pubmed/35084343 ID - info:doi/10.2196/30363 ER - TY - JOUR AU - Siegersma, R. Klaske AU - Evers, Maxime AU - Bots, H. Sophie AU - Groepenhoff, Floor AU - Appelman, Yolande AU - Hofstra, Leonard AU - Tulevski, I. Igor AU - Somsen, Aernout G. AU - den Ruijter, M. Hester AU - Spruit, Marco AU - Onland-Moret, Charlotte N. PY - 2022/1/25 TI - Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching JO - JMIR Med Inform SP - e31063 VL - 10 IS - 1 KW - adverse drug reactions KW - word embeddings KW - clinical notes N2 - Background: Knowledge about adverse drug reactions (ADRs) in the population is limited because of underreporting, which hampers surveillance and assessment of drug safety. Therefore, gathering accurate information that can be retrieved from clinical notes about the incidence of ADRs is of great relevance. However, manual labeling of these notes is time-consuming, and automatization can improve the use of free-text clinical notes for the identification of ADRs. Furthermore, tools for language processing in languages other than English are not widely available. Objective: The aim of this study is to design and evaluate a method for automatic extraction of medication and Adverse Drug Reaction Identification in Clinical Notes (ADRIN). Methods: Dutch free-text clinical notes (N=277,398) and medication registrations (N=499,435) from the Cardiology Centers of the Netherlands database were used. All clinical notes were used to develop word embedding models. Vector representations of word embedding models and string matching with a medical dictionary (Medical Dictionary for Regulatory Activities [MedDRA]) were used for identification of ADRs and medication in a test set of clinical notes that were manually labeled. Several settings, including search area and punctuation, could be adjusted in the prototype to evaluate the optimal version of the prototype. Results: The ADRIN method was evaluated using a test set of 988 clinical notes written on the stop date of a drug. Multiple versions of the prototype were evaluated for a variety of tasks. Binary classification of ADR presence achieved the highest accuracy of 0.84. Reduced search area and inclusion of punctuation improved performance, whereas incorporation of the MedDRA did not improve the performance of the pipeline. Conclusions: The ADRIN method and prototype are effective in recognizing ADRs in Dutch clinical notes from cardiac diagnostic screening centers. Surprisingly, incorporation of the MedDRA did not result in improved identification on top of word embedding models. The implementation of the ADRIN tool may help increase the identification of ADRs, resulting in better care and saving substantial health care costs. UR - https://medinform.jmir.org/2022/1/e31063 UR - http://dx.doi.org/10.2196/31063 UR - http://www.ncbi.nlm.nih.gov/pubmed/35076407 ID - info:doi/10.2196/31063 ER - TY - JOUR AU - Park, Susan AU - Choi, Hyun So AU - Song, Yun-Kyoung AU - Kwon, Jin-Won PY - 2022/1/4 TI - Comparison of Online Patient Reviews and National Pharmacovigilance Data for Tramadol-Related Adverse Events: Comparative Observational Study JO - JMIR Public Health Surveill SP - e33311 VL - 8 IS - 1 KW - drug safety KW - pharmacovigilance KW - tramadol KW - social media KW - adverse effect N2 - Background: Tramadol is known to cause fewer adverse events (AEs) than other opioids. However, recent research has raised concerns about various safety issues. Objective: We aimed to explore these new AEs related to tramadol using social media and conventional pharmacovigilance data. Methods: This study used 2 data sets, 1 from patients? drug reviews on WebMD (January 2007 to January 2021) and 1 from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS; January 2016 to December 2020). We analyzed 2062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities. To analyze AEs from FAERS, a disproportionality analysis was performed with 3 measures: proportional reporting ratio, reporting odds ratio, and information component. Results: From the 869 AEs reported, we identified 125 new signals related to tramadol use not listed on the drug label that satis?ed all 3 signal detection criteria. In addition, 20 serious AEs were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients? symptom descriptions, tramadol-induced pain might also be an unexpected AE. Conclusions: This study detected several novel signals related to tramadol use, suggesting newly identified possible AEs. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data. UR - https://publichealth.jmir.org/2022/1/e33311 UR - http://dx.doi.org/10.2196/33311 UR - http://www.ncbi.nlm.nih.gov/pubmed/34982723 ID - info:doi/10.2196/33311 ER - TY - JOUR AU - Chopard, Daphne AU - Treder, S. Matthias AU - Corcoran, Padraig AU - Ahmed, Nagheen AU - Johnson, Claire AU - Busse, Monica AU - Spasic, Irena PY - 2021/12/24 TI - Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach JO - JMIR Med Inform SP - e28632 VL - 9 IS - 12 KW - natural language processing KW - deep learning KW - machine learning KW - classification N2 - Background: Pharmacovigilance and safety reporting, which involve processes for monitoring the use of medicines in clinical trials, play a critical role in the identification of previously unrecognized adverse events or changes in the patterns of adverse events. Objective: This study aims to demonstrate the feasibility of automating the coding of adverse events described in the narrative section of the serious adverse event report forms to enable statistical analysis of the aforementioned patterns. Methods: We used the Uni?ed Medical Language System (UMLS) as the coding scheme, which integrates 217 source vocabularies, thus enabling coding against other relevant terminologies such as the International Classification of Diseases?10th Revision, Medical Dictionary for Regulatory Activities, and Systematized Nomenclature of Medicine). We used MetaMap, a highly configurable dictionary lookup software, to identify the mentions of the UMLS concepts. We trained a binary classifier using Bidirectional Encoder Representations from Transformers (BERT), a transformer-based language model that captures contextual relationships, to differentiate between mentions of the UMLS concepts that represented adverse events and those that did not. Results: The model achieved a high F1 score of 0.8080, despite the class imbalance. This is 10.15 percent points lower than human-like performance but also 17.45 percent points higher than that of the baseline approach. Conclusions: These results confirmed that automated coding of adverse events described in the narrative section of serious adverse event reports is feasible. Once coded, adverse events can be statistically analyzed so that any correlations with the trialed medicines can be estimated in a timely fashion. UR - https://medinform.jmir.org/2021/12/e28632 UR - http://dx.doi.org/10.2196/28632 UR - http://www.ncbi.nlm.nih.gov/pubmed/34951601 ID - info:doi/10.2196/28632 ER - TY - JOUR AU - Wang, Y. Elizabeth AU - Breyer, N. Benjamin AU - Lee, W. Austin AU - Rios, Natalie AU - Oni-Orisan, Akinyemi AU - Steinman, A. Michael AU - Sim, Ida AU - Kenfield, A. Stacey AU - Bauer, R. Scott PY - 2021/12/24 TI - Perceptions of Older Men Using a Mobile Health App to Monitor Lower Urinary Tract Symptoms and Tamsulosin Side Effects: Mixed Methods Study JO - JMIR Hum Factors SP - e30767 VL - 8 IS - 4 KW - BPH KW - mobile health KW - mHealth KW - telehealth KW - telemedicine N2 - Background: Mobile health (mHealth) apps may provide an efficient way for patients with lower urinary tract symptoms (LUTS) to log and communicate symptoms and medication side effects with their clinicians. Objective: The aim of this study was to explore the perceptions of older men with LUTS after using an mHealth app to track their symptoms and tamsulosin side effects. Methods: Structured phone interviews were conducted after a 2-week study piloting the daily use of a mobile app to track the severity of patient-selected LUTS and tamsulosin side effects. Quantitative and qualitative data were considered. Results: All 19 (100%) pilot study participants completed the poststudy interviews. Most of the men (n=13, 68%) reported that the daily questionnaires were the right length, with 32% (n=6) reporting that the questionnaires were too short. Men with more severe symptoms were less likely to report changes in perception of health or changes in self-management; 47% (n=9) of the men reported improved awareness of symptoms and 5% (n=1) adjusted fluid intake based on the questionnaire. All of the men were willing to share app data with their clinicians. Thematic analysis of qualitative data yielded eight themes: (1) orientation (setting up app, format, symptom selection, and side-effect selection), (2) triggers (routine or habit and symptom timing), (3) daily questionnaire (reporting symptoms, reporting side effects, and tailoring), (4) technology literacy, (5) perceptions (awareness, causation or relevance, data quality, convenience, usefulness, and other apps), (6) self-management, (7) clinician engagement (communication and efficiency), and (8) improvement (reference materials, flexibility, language, management recommendations, and optimize clinician engagement). Conclusions: We assessed the perceptions of men using an mHealth app to monitor and improve management of LUTS and medication side effects. LUTS management may be further optimized by tailoring the mobile app experience to meet patients? individual needs, such as tracking a greater number of symptoms and integrating the app with clinicians? visits. mHealth apps are likely a scalable modality to monitor symptoms and improve care of older men with LUTS. Further study is required to determine the best ways to tailor the mobile app and to communicate data to clinicians or incorporate data into the electronical medical record meaningfully. UR - https://humanfactors.jmir.org/2021/4/e30767 UR - http://dx.doi.org/10.2196/30767 UR - http://www.ncbi.nlm.nih.gov/pubmed/34951599 ID - info:doi/10.2196/30767 ER - TY - JOUR AU - Passardi, Alessandro AU - Serra, Patrizia AU - Donati, Caterina AU - Fiori, Federica AU - Prati, Sabrina AU - Vespignani, Roberto AU - Taglioni, Gabriele AU - Farfaneti Ghetti, Patrizia AU - Martinelli, Giovanni AU - Nanni, Oriana AU - Altini, Mattia AU - Frassineti, Luca Giovanni AU - Minguzzi, Vittoria Martina PY - 2021/12/20 TI - An Integrated Model to Improve Medication Reconciliation in Oncology: Prospective Interventional Study JO - J Med Internet Res SP - e31321 VL - 23 IS - 12 KW - medication recognition KW - medication reconciliation KW - IT platform KW - community pharmacies KW - healthcare transitions KW - pharmacy KW - oncology KW - drug incompatibility KW - information technology KW - drug interactions N2 - Background: Accurate medication reconciliation reduces the risk of drug incompatibilities and adverse events that can occur during transitions in care. Community pharmacies (CPs) are a crucial part of the health care system and could be involved in collecting essential information on conventional and supplementary drugs used at home. Objective: The aim of this paper was to establish an alliance between our cancer institute, Istituto Romagnolo per lo Studio dei Tumori (IRST), and CPs, the latter entrusted with the completion of a pharmacological recognition survey. We also aimed to integrate the national information technology (IT) platform of CPs with the electronic medical records of IRST. Methods: Cancer patients undergoing antiblastic treatments were invited to select a CP taking part in the study and to complete the pharmacological recognition step. The information collected by the pharmacist was sent to the electronic medical records of IRST through the new IT platform, after which the oncologist performed the reconciliation process. Results: A total of 66 CPs completed surveys for 134 patients. An average of 5.9 drugs per patient was used at home, with 12 or more used in the most advanced age groups. Moreover, 60% (80/134) of the patients used nonconventional products or critical foods. Some potential interactions between nonconventional medications and cancer treatments were reported. Conclusions: In the PROF-1 (Progetto di Rete in Oncologia con le Farmacie di comunità della Romagna) study, an alliance was created between our cancer center and CPs to improve medication reconciliation, and a new integrated IT platform was validated. Trial Registration: ClinicalTrials.gov NCT04796142; https://clinicaltrials.gov/ct2/show/NCT04796142 UR - https://www.jmir.org/2021/12/e31321 UR - http://dx.doi.org/10.2196/31321 UR - http://www.ncbi.nlm.nih.gov/pubmed/34932001 ID - info:doi/10.2196/31321 ER - TY - JOUR AU - Bannay, Aurélie AU - Bories, Mathilde AU - Le Corre, Pascal AU - Riou, Christine AU - Lemordant, Pierre AU - Van Hille, Pascal AU - Chazard, Emmanuel AU - Dode, Xavier AU - Cuggia, Marc AU - Bouzillé, Guillaume PY - 2021/12/13 TI - Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case JO - JMIR Med Inform SP - e29286 VL - 9 IS - 12 KW - drug interactions KW - statins KW - administrative claims KW - health care KW - big data KW - data linking KW - data warehousing N2 - Background: Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]). Objective: This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions. Methods: A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay. Results: The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P<.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization. Conclusions: This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data. UR - https://medinform.jmir.org/2021/12/e29286 UR - http://dx.doi.org/10.2196/29286 UR - http://www.ncbi.nlm.nih.gov/pubmed/34898457 ID - info:doi/10.2196/29286 ER - TY - JOUR AU - Chan, Erina AU - Small, S. Serena AU - Wickham, E. Maeve AU - Cheng, Vicki AU - Balka, Ellen AU - Hohl, M. Corinne PY - 2021/12/10 TI - The Utility of Different Data Standards to Document Adverse Drug Event Symptoms and Diagnoses: Mixed Methods Study JO - J Med Internet Res SP - e27188 VL - 23 IS - 12 KW - adverse drug events KW - health information technology KW - data standards N2 - Background: Existing systems to document adverse drug events often use free text data entry, which produces nonstandardized and unstructured data that are prone to misinterpretation. Standardized terminology may improve data quality; however, it is unclear which data standard is most appropriate for documenting adverse drug event symptoms and diagnoses. Objective: This study aims to compare the utility, strengths, and weaknesses of different data standards for documenting adverse drug event symptoms and diagnoses. Methods: We performed a mixed methods substudy of a multicenter retrospective chart review. We reviewed the research records of prospectively diagnosed adverse drug events at 5 Canadian hospitals. A total of 2 pharmacy research assistants independently entered the symptoms and diagnoses for the adverse drug events using four standards: Medical Dictionary for Regulatory Activities (MedDRA), Systematized Nomenclature of Medicine (SNOMED) Clinical Terms, SNOMED Adverse Reaction (SNOMED ADR), and International Classification of Diseases (ICD) 11th Revision. Disagreements between research assistants regarding the case-specific utility of data standards were discussed until a consensus was reached. We used consensus ratings to determine the proportion of adverse drug events covered by a data standard and coded and analyzed field notes from the consensus sessions. Results: We reviewed 573 adverse drug events and found that MedDRA and ICD-11 had excellent coverage of adverse drug event symptoms and diagnoses. MedDRA had the highest number of matches between the research assistants, whereas ICD-11 had the fewest. SNOMED ADR had the lowest proportion of adverse drug event coverage. The research assistants were most likely to encounter terminological challenges with SNOMED ADR and usability challenges with ICD-11, whereas least likely to encounter challenges with MedDRA. Conclusions: Usability, comprehensiveness, and accuracy are important features of data standards for documenting adverse drug event symptoms and diagnoses. On the basis of our results, we recommend the use of MedDRA. UR - https://www.jmir.org/2021/12/e27188 UR - http://dx.doi.org/10.2196/27188 UR - http://www.ncbi.nlm.nih.gov/pubmed/34890351 ID - info:doi/10.2196/27188 ER - TY - JOUR AU - Mosa, Mohammad Abu Saleh AU - Rana, Zaman Md Kamruz AU - Islam, Humayera AU - Hossain, Mosharraf A. K. M. AU - Yoo, Illhoi PY - 2021/12/2 TI - A Smartphone-Based Decision Support Tool for Predicting Patients at Risk of Chemotherapy-Induced Nausea and Vomiting: Retrospective Study on App Development Using Decision Tree Induction JO - JMIR Mhealth Uhealth SP - e27024 VL - 9 IS - 12 KW - chemotherapy KW - CINV risk factors KW - data mining KW - prediction KW - decision trees KW - clinical decision support KW - smartphone app N2 - Background: Chemotherapy-induced nausea and vomiting (CINV) are the two most frightful and unpleasant side effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences. Objective: This study compared the performances of different data mining models for predicting the risk of CINV among the patients and developed a smartphone app for clinical decision support to recommend the risk of CINV at the point of care. Methods: Data were collected by retrospective record review from the electronic medical records used at the University of Missouri Ellis Fischel Cancer Center. Patients who received chemotherapy and standard antiemetics at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent data sets of patients based on emetogenicity (low, moderate, and high) and two phases of CINV (acute and delayed). A total of 14 risk factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (1) naive Bayes algorithm, (2) logistic regression classifier, (3) neural network, (4) support vector machine (using sequential minimal optimization), and (5) decision tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the decision tree algorithm, which conforms to the criteria of explainable, usable, and actionable artificial intelligence. The app was created using both the bulk questionnaire approach and the adaptive approach. Results: The decision tree performed well in both phases of high emetogenic chemotherapies, with a significant margin compared to the other algorithms. The accuracy measure for the six patient groups ranged from 79.3% to 94.8%. The app was developed using the results from the decision tree because of its consistent performance and simple, explainable nature. The bulk questionnaire approach asks 14 questions in the smartphone app, while the adaptive approach can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point of care. Conclusions: This study solved a real clinical problem, and the solution can be used for personalized and precise evidence-based CINV management, leading to a better life quality for patients and reduced health care costs. UR - https://mhealth.jmir.org/2021/12/e27024 UR - http://dx.doi.org/10.2196/27024 UR - http://www.ncbi.nlm.nih.gov/pubmed/34860677 ID - info:doi/10.2196/27024 ER - TY - JOUR AU - Wu, Hong AU - Ji, Jiatong AU - Tian, Haimei AU - Chen, Yao AU - Ge, Weihong AU - Zhang, Haixia AU - Yu, Feng AU - Zou, Jianjun AU - Nakamura, Mitsuhiro AU - Liao, Jun PY - 2021/12/1 TI - Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding?Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model JO - JMIR Med Inform SP - e26407 VL - 9 IS - 12 KW - deep learning KW - BERT KW - adverse drug reaction KW - named entity recognition KW - electronic medical records N2 - Background: With the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance. Objective: This study describes how to identify ADR-related information from Chinese ADE reports. Methods: Our study established an efficient automated tool, named BBC-Radical. BBC-Radical is a model that consists of 3 components: Bidirectional Encoder Representations from Transformers (BERT), bidirectional long short-term memory (bi-LSTM), and conditional random field (CRF). The model identifies ADR-related information from Chinese ADR reports. Token features and radical features of Chinese characters were used to represent the common meaning of a group of words. BERT and Bi-LSTM-CRF were novel models that combined these features to conduct named entity recognition (NER) tasks in the free-text section of 24,890 ADR reports from the Jiangsu Province Adverse Drug Reaction Monitoring Center from 2010 to 2016. Moreover, the man-machine comparison experiment on the ADE records from Drum Tower Hospital was designed to compare the NER performance between the BBC-Radical model and a manual method. Results: The NER model achieved relatively high performance, with a precision of 96.4%, recall of 96.0%, and F1 score of 96.2%. This indicates that the performance of the BBC-Radical model (precision 87.2%, recall 85.7%, and F1 score 86.4%) is much better than that of the manual method (precision 86.1%, recall 73.8%, and F1 score 79.5%) in the recognition task of each kind of entity. Conclusions: The proposed model was competitive in extracting ADR-related information from ADE reports, and the results suggest that the application of our method to extract ADR-related information is of great significance in improving the quality of ADR reports and postmarketing drug safety evaluation. UR - https://medinform.jmir.org/2021/12/e26407 UR - http://dx.doi.org/10.2196/26407 UR - http://www.ncbi.nlm.nih.gov/pubmed/34855616 ID - info:doi/10.2196/26407 ER - TY - JOUR AU - Jarynowski, Andrzej AU - Semenov, Alexander AU - Kami?ski, Miko?aj AU - Belik, Vitaly PY - 2021/11/29 TI - Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning JO - J Med Internet Res SP - e30529 VL - 23 IS - 11 KW - adverse events KW - Sputnik V KW - Gam-COVID-Vac KW - social media KW - Telegram KW - COVID-19 KW - Sars-CoV-2 KW - deep learning KW - vaccine safety N2 - Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) ?DeepPavlov,? which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (?=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines. UR - https://www.jmir.org/2021/11/e30529 UR - http://dx.doi.org/10.2196/30529 UR - http://www.ncbi.nlm.nih.gov/pubmed/34662291 ID - info:doi/10.2196/30529 ER - TY - JOUR AU - Mitra, Avijit AU - Ahsan, Hiba AU - Li, Wenjun AU - Liu, Weisong AU - Kerns, D. Robert AU - Tsai, Jack AU - Becker, William AU - Smelson, A. David AU - Yu, Hong PY - 2021/11/8 TI - Risk Factors Associated With Nonfatal Opioid Overdose Leading to Intensive Care Unit Admission: A Cross-sectional Study JO - JMIR Med Inform SP - e32851 VL - 9 IS - 11 KW - opioids KW - overdose KW - risk factors KW - electronic health records KW - social and behavioral determinants of health KW - natural language processing KW - intensive care unit N2 - Background: Opioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem. Objective: The objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD. Methods: We performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR). Results: The strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95% CI 0.44-0.96; >64 years: aOR 0.16, 95% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82% of all SBDH information was identified by the NLP model, in contrast to only 0.18% identified by the ICD codes. Conclusions: This is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic. UR - https://medinform.jmir.org/2021/11/e32851 UR - http://dx.doi.org/10.2196/32851 UR - http://www.ncbi.nlm.nih.gov/pubmed/34747714 ID - info:doi/10.2196/32851 ER - TY - JOUR AU - Teramoto, Kei AU - Takeda, Toshihiro AU - Mihara, Naoki AU - Shimai, Yoshie AU - Manabe, Shirou AU - Kuwata, Shigeki AU - Kondoh, Hiroshi AU - Matsumura, Yasushi PY - 2021/11/1 TI - Detecting Adverse Drug Events Through the Chronological Relationship Between the Medication Period and the Presence of Adverse Reactions From Electronic Medical Record Systems: Observational Study JO - JMIR Med Inform SP - e28763 VL - 9 IS - 11 KW - real world data KW - electronic medical record KW - adverse drug event N2 - Background: Medicines may cause various adverse reactions. An enormous amount of money and effort is spent investigating adverse drug events (ADEs) in clinical trials and postmarketing surveillance. Real-world data from multiple electronic medical records (EMRs) can make it easy to understand the ADEs that occur in actual patients. Objective: In this study, we generated a patient medication history database from physician orders recorded in EMRs, which allowed the period of medication to be clearly identified. Methods: We developed a method for detecting ADEs based on the chronological relationship between the presence of an adverse event and the medication period. To verify our method, we detected ADEs with alanine aminotransferase elevation in patients receiving aspirin, clopidogrel, and ticlopidine. The accuracy of the detection was evaluated with a chart review and by comparison with the Roussel Uclaf Causality Assessment Method (RUCAM), which is a standard method for detecting drug-induced liver injury. Results: The calculated rates of ADE with ALT elevation in patients receiving aspirin, clopidogrel, and ticlopidine were 3.33% (868/26,059 patients), 3.70% (188/5076 patients), and 5.69% (226/3974 patients), respectively, which were in line with the rates of previous reports. We reviewed the medical records of the patients in whom ADEs were detected. Our method accurately predicted ADEs in 90% (27/30patients) treated with aspirin, 100% (9/9 patients) treated with clopidogrel, and 100% (4/4 patients) treated with ticlopidine. Only 3 ADEs that were detected by the RUCAM were not detected by our method. Conclusions: These findings demonstrate that the present method is effective for detecting ADEs based on EMR data. UR - https://medinform.jmir.org/2021/11/e28763 UR - http://dx.doi.org/10.2196/28763 UR - http://www.ncbi.nlm.nih.gov/pubmed/33993103 ID - info:doi/10.2196/28763 ER - TY - JOUR AU - Wang, Jie-Teng AU - Lin, Wen-Yang PY - 2021/10/28 TI - Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation JO - JMIR Med Inform SP - e28752 VL - 9 IS - 10 KW - adverse drug reaction KW - data anonymization KW - incremental data publishing KW - privacy preserving data publishing KW - spontaneous reporting system KW - drug KW - data set KW - anonymous KW - privacy KW - security KW - algorithm KW - development KW - validation KW - data N2 - Background: Spontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals? privacy. We have previously proposed a privacy model called MS(k, ?*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, ?*)-bounding. Objective: We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data. Methods: We identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, ?*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio). Results: The best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal. Conclusions: The proposed PPMS(k, ?*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection. UR - https://medinform.jmir.org/2021/10/e28752 UR - http://dx.doi.org/10.2196/28752 UR - http://www.ncbi.nlm.nih.gov/pubmed/34709197 ID - info:doi/10.2196/28752 ER - TY - JOUR AU - Rizk, Elsie AU - Swan, T. Joshua PY - 2021/10/25 TI - Development, Validation, and Assessment of Clinical Impact of Real-time Alerts to Detect Inpatient As-Needed Opioid Orders With Duplicate Indications: Prospective Study JO - J Med Internet Res SP - e28235 VL - 23 IS - 10 KW - opioid stewardship KW - pain KW - as-needed opioids KW - duplicate orders KW - automated alerts N2 - Background: As-needed (PRN) opioid orders with duplicate indications can lead to medication errors and opioid-related adverse drug events. Objective: The objective of our study was to build and validate real-time alerts that detect duplicate PRN opioid orders and assist clinicians in optimizing the safety of opioid orders. Methods: This single-center, prospective study used an iterative, 3-step process to refine alert performance by advancing from small sample evaluations of positive predictive values (PPVs) (step 1) through intensive evaluations of accuracy (step 2) to evaluations of clinical impact (step 3). Validation cohorts were randomly sampled from eligible patients for each step. Results: During step 1, the PPV was 100% (one-sided, 97.5% CI 70%-100%) for moderate and severe pain alerts. During step 2, duplication of 1 or more PRN opioid orders was identified for 17% (34/201; 95% CI, 12%-23%) of patients during chart review. This bundle of alerts showed 94% sensitivity (95% CI 80%-99%) and 96% specificity (95% CI 92%-98%) for identifying patients who had duplicate PRN opioid orders. During step 3, at least 1 intervention was made to the medication profile for 77% (46/60; 95% CI 64%-87%) of patients, and at least 1 inappropriate duplicate PRN opioid order was discontinued for 53% (32/60; 95% CI 40%-66%) of patients. Conclusions: The bundle of alerts developed in this study was validated against chart review by a pharmacist and identified patients who benefited from medication safety interventions to optimize PRN opioid orders. UR - https://www.jmir.org/2021/10/e28235 UR - http://dx.doi.org/10.2196/28235 UR - http://www.ncbi.nlm.nih.gov/pubmed/34694235 ID - info:doi/10.2196/28235 ER - TY - JOUR AU - Lavertu, Adam AU - Hamamsy, Tymor AU - Altman, B. Russ PY - 2021/10/21 TI - Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis JO - J Med Internet Res SP - e27714 VL - 23 IS - 10 KW - social media for health KW - pharmacovigilance KW - adverse drug reactions KW - machine learning KW - network analysis KW - word embeddings KW - drug safety KW - social media N2 - Background: Adverse drug reactions (ADRs) affect the health of hundreds of thousands of individuals annually in the United States, with associated costs of hundreds of billions of dollars. The monitoring and analysis of the severity of ADRs is limited by the current qualitative and categorical systems of severity classification. Previous efforts have generated quantitative estimates for a subset of ADRs but were limited in scope because of the time and costs associated with the efforts. Objective: The aim of this study is to increase the number of ADRs for which there are quantitative severity estimates while improving the quality of these severity estimates. Methods: We present a semisupervised approach that estimates ADR severity by using social media word embeddings to construct a lexical network of ADRs and perform label propagation. We used this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from the Medical Dictionary for Regulatory Activities. Results: Our Severity of Adverse Events Derived from Reddit (SAEDR) scores have good correlations with real-world outcomes. The SAEDR scores had Spearman correlations of 0.595, 0.633, and ?0.748 for death, serious outcome, and no outcome, respectively, with ADR case outcomes in the Food and Drug Administration Adverse Event Reporting System. We investigated different methods for defining initial seed term sets and evaluated their impact on the severity estimates. We analyzed severity distributions for ADRs based on their appearance in boxed warning drug label sections, as well as for ADRs with sex-specific associations. We found that ADRs discovered in the postmarketing period had significantly greater severity than those discovered during the clinical trial (P<.001). We created quantitative drug-risk profile (DRIP) scores for 968 drugs that had a Spearman correlation of 0.377 with drugs ranked by the Food and Drug Administration Adverse Event Reporting System cases resulting in death, where the given drug was the primary suspect. Conclusions: Our SAEDR and DRIP scores are well correlated with the real-world outcomes of the entities they represent and have demonstrated utility in pharmacovigilance research. We make the SAEDR scores for 12,198 ADRs and the DRIP scores for 968 drugs publicly available to enable more quantitative analysis of pharmacovigilance data. UR - https://www.jmir.org/2021/10/e27714 UR - http://dx.doi.org/10.2196/27714 UR - http://www.ncbi.nlm.nih.gov/pubmed/34673524 ID - info:doi/10.2196/27714 ER - TY - JOUR AU - Frew, M. Paula AU - Randall, A. Laura AU - King, R. Adrian AU - Schamel, T. Jay AU - Spaulding, C. Anne AU - AU - Holloway, W. Ian PY - 2021/9/10 TI - Health Behavior Survey Among People Who Use Opioids: Protocol for Implementing Technology-Based Rapid Response Surveillance in Community Settings JO - JMIR Res Protoc SP - e25575 VL - 10 IS - 9 KW - substance use KW - opioid KW - opioid crisis KW - social determinants KW - hidden populations KW - health equity N2 - Background: In 2018, 2 million Americans met the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition diagnostic criteria for an opioid use disorder, and 9.9 million Americans had misused prescription pain relievers the previous year. Despite a rapid increase in opioid misuse, opioid use disorders, and overdoses, data are limited on the behavioral and contextual risks as well as the protective factors fueling the opioid epidemic in some hard hit US cities?Atlanta, Los Angeles, and Las Vegas. Opioid use also contributes to the risk of other health problems such as HIV and hepatitis C virus infections or mental health disorders and is linked to behavioral and environmental risks (eg, homelessness, experiences of violence, involvement in the justice system). Knowledge of the relationships between these linked vulnerabilities and how they influence service utilization is critical to effective policy and interventions. Objective: This survey explores the relationships between demographic and economic characteristics, behavioral and environmental risk factors, and service utilization of people who use opioids to inform public health practice, policy, and future efforts to mitigate the risks faced by this population experiencing multiple health, social, and economic vulnerabilities. The results of this survey will be used to identify needs and intervention points for people who use drugs currently served by public health organizations. Methods: We implemented a community-engaged strategy that involved development and execution of a two-stage purposive sampling plan involving selection of partner organizations (syringe exchange programs in urban settings) and recruitment and enrollment of participants aged 18-69 years served by these organizations in Atlanta, Los Angeles, and Las Vegas from 2019 to 2020. The recruited participants completed a survey, including a variety of measures to assess health (physical and mental) and health behaviors such as sexual behavior, vaccine receipt, and HIV/ hepatitis C virus infection testing. Additional items assessed drug use and misuse, syringe exchange and health service utilization, sex exchange, histories of interpersonal violence, and vaccine confidence. Results: This protocol was successfully implemented despite challenges such as real-time technology issues and rapidly finding and surveying a difficult-to-reach population. We sampled 1127 unique participants (248 in Atlanta, 465 in Los Angeles, and 414 in Las Vegas). Conclusions: The establishment and utilization of strong community partnerships enabled the rapid collection of data from a typically difficult-to-reach population. Local efforts such as these are needed to develop policies and practices that promote harm reduction among people who use opioids. International Registered Report Identifier (IRRID): RR1-10.2196/25575 UR - https://www.researchprotocols.org/2021/9/e25575 UR - http://dx.doi.org/10.2196/25575 UR - http://www.ncbi.nlm.nih.gov/pubmed/34505834 ID - info:doi/10.2196/25575 ER - TY - JOUR AU - Bright, A. Roselie AU - Rankin, K. Summer AU - Dowdy, Katherine AU - Blok, V. Sergey AU - Bright, J. Susan AU - Palmer, M. Lee Anne PY - 2021/8/11 TI - Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method JO - JMIRx Med SP - e27017 VL - 2 IS - 3 KW - epidemiology KW - electronic health record KW - electronic health care record KW - big data KW - patient harm KW - patient safety KW - public health KW - product surveillance, postmarketing KW - natural language processing KW - proof-of-concept study KW - critical care N2 - Background: Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome (?attributed?) or state the simple treatment and outcome without an association (?unattributed?). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, ?transfusion? and ?time-based.? Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians? documentation of attributed AEs. Objective: We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs. Methods: We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs. Results: Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination. Conclusions: The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process. UR - https://med.jmirx.org/2021/3/e27017 UR - http://dx.doi.org/10.2196/27017 UR - http://www.ncbi.nlm.nih.gov/pubmed/37725533 ID - info:doi/10.2196/27017 ER - TY - JOUR AU - Matsuda, Shinichi AU - Ohtomo, Takumi AU - Tomizawa, Shiho AU - Miyano, Yuki AU - Mogi, Miwako AU - Kuriki, Hiroshi AU - Nakayama, Terumi AU - Watanabe, Shinichi PY - 2021/6/29 TI - Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus JO - JMIR Public Health Surveill SP - e29238 VL - 7 IS - 6 KW - social media KW - adverse drug reaction KW - pharmacovigilance KW - text mining KW - systemic lupus erythematosus KW - natural language processing KW - NLP KW - lupus KW - chronic disease KW - narrative KW - insurance KW - data KW - epidemiology KW - burden KW - Japan KW - patient-generated N2 - Background: Gaining insights that cannot be obtained from health care databases from patients has become an important topic in pharmacovigilance. Objective: Our objective was to demonstrate a use case, in which patient-generated data were incorporated in pharmacovigilance, to understand the epidemiology and burden of illness in Japanese patients with systemic lupus erythematosus. Methods: We used data on systemic lupus erythematosus, an autoimmune disease that substantially impairs quality of life, from 2 independent data sets. To understand the disease?s epidemiology, we analyzed a Japanese health insurance claims database. To understand the disease?s burden, we analyzed text data collected from Japanese disease blogs (t?by?ki) written by patients with systemic lupus erythematosus. Natural language processing was applied to these texts to identify frequent patient-level complaints, and term frequency?inverse document frequency was used to explore patient burden during treatment. We explored health-related quality of life based on patient descriptions. Results: We analyzed data from 4694 and 635 patients with systemic lupus erythematosus in the health insurance claims database and t?by?ki blogs, respectively. Based on health insurance claims data, the prevalence of systemic lupus erythematosus is 107.70 per 100,000 persons. T?by?ki text data analysis showed that pain-related words (eg, pain, severe pain, arthralgia) became more important after starting treatment. We also found an increase in patients? references to mobility and self-care over time, which indicated increased attention to physical disability due to disease progression. Conclusions: A classical medical database represents only a part of a patient's entire treatment experience, and analysis using solely such a database cannot represent patient-level symptoms or patient concerns about treatments. This study showed that analysis of t?by?ki blogs can provide added information on patient-level details, advancing patient-centric pharmacovigilance. UR - https://publichealth.jmir.org/2021/6/e29238 UR - http://dx.doi.org/10.2196/29238 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255719 ID - info:doi/10.2196/29238 ER - TY - JOUR AU - Milne-Ives, Madison AU - Lam, Ching AU - Rehman, Najib AU - Sharif, Raja AU - Meinert, Edward PY - 2021/6/10 TI - Distributed Ledger Infrastructure to Verify Adverse Event Reporting (DeLIVER): Proposal for a Proof-of-Concept Study JO - JMIR Res Protoc SP - e28616 VL - 10 IS - 6 KW - adverse drug reaction reporting systems KW - drug-related side effects and adverse reactions KW - blockchain KW - mobile applications KW - distributed ledger technology N2 - Background: Adverse drug event reporting is critical for ensuring patient safety; however, numbers of reports have been declining. There is a need for a more user-friendly reporting system and for a means of verifying reports that have been filed. Objective: This project has 2 main objectives: (1) to identify the perceived benefits and barriers in the current reporting of adverse events by patients and health care providers and (2) to develop a distributed ledger infrastructure and user interface to collect and collate adverse event reports to create a comprehensive and interoperable database. Methods: A review of the literature will be conducted to identify the strengths and limitations of the current UK adverse event reporting system (the Yellow Card System). If insufficient information is found in this review, a survey will be created to collect data from system users. The results of these investigations will be incorporated into the development of a mobile and web app for adverse event reporting. A digital infrastructure will be built using distributed ledger technology to provide a means of linking reports with existing pharmaceutical tracking systems. Results: The key outputs of this project will be the development of a digital infrastructure, including a backend distributed ledger system and an app-based user interface. Conclusions: This infrastructure is expected to improve the accuracy and efficiency of adverse event reporting systems by enabling the monitoring of specific medicines or medical devices over their life course while protecting patients? personal health data. International Registered Report Identifier (IRRID): PRR1-10.2196/28616 UR - https://www.researchprotocols.org/2021/6/e28616 UR - http://dx.doi.org/10.2196/28616 UR - http://www.ncbi.nlm.nih.gov/pubmed/34110292 ID - info:doi/10.2196/28616 ER - TY - JOUR AU - Yang, Hsuan-Chia AU - Islam, Mohaimenul Md AU - Nguyen, Alex Phung Anh AU - Wang, Ching-Huan AU - Poly, Nasrin Tahmina AU - Huang, Chih-Wei AU - Li, Jack Yu-Chuan PY - 2021/2/15 TI - Development of a Web-Based System for Exploring Cancer Risk With Long-term Use of Drugs: Logistic Regression Approach JO - JMIR Public Health Surveill SP - e21401 VL - 7 IS - 2 KW - cancer KW - risk KW - prevention KW - chemoprevention KW - long-term?use drugs KW - drug KW - epidemiology KW - temporal model KW - modeling KW - web-based system N2 - Background: Existing epidemiological evidence regarding the association between the long-term use of drugs and cancer risk remains controversial. Objective: We aimed to have a comprehensive view of the cancer risk of the long-term use of drugs. Methods: A nationwide population-based, nested, case-control study was conducted within the National Health Insurance Research Database sample cohort of 1999 to 2013 in Taiwan. We identified cases in adults aged 20 years and older who were receiving treatment for at least two months before the index date. We randomly selected control patients from the patients without a cancer diagnosis during the 15 years (1999-2013) of the study period. Case and control patients were matched 1:4 based on age, sex, and visit date. Conditional logistic regression was used to estimate the association between drug exposure and cancer risk by adjusting potential confounders such as drugs and comorbidities. Results: There were 79,245 cancer cases and 316,980 matched controls included in this study. Of the 45,368 associations, there were 2419, 1302, 662, and 366 associations found statistically significant at a level of P<.05, P<.01, P<.001, and P<.0001, respectively. Benzodiazepine derivatives were associated with an increased risk of brain cancer (adjusted odds ratio [AOR] 1.379, 95% CI 1.138-1.670; P=.001). Statins were associated with a reduced risk of liver cancer (AOR 0.470, 95% CI 0.426-0.517; P<.0001) and gastric cancer (AOR 0.781, 95% CI 0.678-0.900; P<.001). Our web-based system, which collected comprehensive data of associations, contained 2 domains: (1) the drug and cancer association page and (2) the overview page. Conclusions: Our web-based system provides an overview of comprehensive quantified data of drug-cancer associations. With all the quantified data visualized, the system is expected to facilitate further research on cancer risk and prevention, potentially serving as a stepping-stone to consulting and exploring associations between the long-term use of drugs and cancer risk. UR - http://publichealth.jmir.org/2021/2/e21401/ UR - http://dx.doi.org/10.2196/21401 UR - http://www.ncbi.nlm.nih.gov/pubmed/33587043 ID - info:doi/10.2196/21401 ER - TY - JOUR AU - Kirkendall, Eric AU - Huth, Hannah AU - Rauenbuehler, Benjamin AU - Moses, Adam AU - Melton, Kristin AU - Ni, Yizhao PY - 2020/12/2 TI - The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis JO - JMIR Med Inform SP - e22031 VL - 8 IS - 12 KW - medication administration KW - error KW - automated algorithm KW - generalizability KW - quantitative comparative analysis KW - discrepancy KW - detection KW - quantitative analysis KW - portability KW - performance algorithm KW - electronic health record N2 - Background: As a result of the overwhelming proportion of medication errors occurring each year, there has been an increased focus on developing medication error prevention strategies. Recent advances in electronic health record (EHR) technologies allow institutions the opportunity to identify medication administration error events in real time through computerized algorithms. MED.Safe, a software package comprising medication discrepancy detection algorithms, was developed to meet this need by performing an automated comparison of medication orders to medication administration records (MARs). In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site. Objective: The purpose of this study is to determine the portability and generalizability of the MED.Safe software at a second site by assessing the performance and fit of the algorithms through comparison of discrepancy rates and other metrics across institutions. Methods: The MED.Safe software package was executed on medication use data from the implementation site to generate prescribing ratios and discrepancy rates. A retrospective analysis of medication prescribing and documentation patterns was then performed on the results and compared to those from the development site to determine the algorithmic performance and fit. Variance in performance from the development site was further explored and characterized. Results: Compared to the development site, the implementation site had lower audit/order ratios and higher MAR/(order + audit) ratios. The discrepancy rates on the implementation site were consistently higher than those from the development site. Three drivers for the higher discrepancy rates were alternative clinical workflow using orders with dosing ranges; a data extract, transfer, and load issue causing modified order data to overwrite original order values in the EHRs; and delayed EHR documentation of verbal orders. Opportunities for improvement were identified and applied using a software update, which decreased false-positive discrepancies and improved overall fit. Conclusions: The execution of MED.Safe at a second site was feasible and effective in the detection of medication administration discrepancies. A comparison of medication ordering, administration, and discrepancy rates identified areas where MED.Safe could be improved through customization. One modification of MED.Safe through deployment of a software update improved the overall algorithmic fit at the implementation site. More flexible customizations to accommodate different clinical practice patterns could improve MED.Safe?s fit at new sites. UR - https://medinform.jmir.org/2020/12/e22031 UR - http://dx.doi.org/10.2196/22031 UR - http://www.ncbi.nlm.nih.gov/pubmed/33263548 ID - info:doi/10.2196/22031 ER - TY - JOUR AU - Ujiie, Shogo AU - Yada, Shuntaro AU - Wakamiya, Shoko AU - Aramaki, Eiji PY - 2020/11/27 TI - Identification of Adverse Drug Event?Related Japanese Articles: Natural Language Processing Analysis JO - JMIR Med Inform SP - e22661 VL - 8 IS - 11 KW - adverse drug events KW - medical informatics KW - natural language processing KW - pharmacovigilance N2 - Background: Medical articles covering adverse drug events (ADEs) are systematically reported by pharmaceutical companies for drug safety information purposes. Although policies governing reporting to regulatory bodies vary among countries and regions, all medical article reporting may be categorized as precision or recall based. Recall-based reporting, which is implemented in Japan, requires the reporting of any possible ADE. Therefore, recall-based reporting can introduce numerous false negatives or substantial amounts of noise, a problem that is difficult to address using limited manual labor. Objective: Our aim was to develop an automated system that could identify ADE-related medical articles, support recall-based reporting, and alleviate manual labor in Japanese pharmaceutical companies. Methods: Using medical articles as input, our system based on natural language processing applies document-level classification to extract articles containing ADEs (replacing manual labor in the first screening) and sentence-level classification to extract sentences within those articles that imply ADEs (thus supporting experts in the second screening). We used 509 Japanese medical articles annotated by a medical engineer to evaluate the performance of the proposed system. Results: Document-level classification yielded an F1 of 0.903. Sentence-level classification yielded an F1 of 0.413. These were averages of fivefold cross-validations. Conclusions: A simple automated system may alleviate the manual labor involved in screening drug safety?related medical articles in pharmaceutical companies. After improving the accuracy of the sentence-level classification by considering a wider context, we intend to apply this system toward real-world postmarketing surveillance. UR - http://medinform.jmir.org/2020/11/e22661/ UR - http://dx.doi.org/10.2196/22661 UR - http://www.ncbi.nlm.nih.gov/pubmed/33245290 ID - info:doi/10.2196/22661 ER - TY - JOUR AU - Sragow, Michael Howard AU - Bidell, Eileen AU - Mager, Douglas AU - Grannis, Shaun PY - 2020/11/20 TI - Universal Patient Identifier and Interoperability for Detection of Serious Drug Interactions: Retrospective Study JO - JMIR Med Inform SP - e23353 VL - 8 IS - 11 KW - patient identification KW - pharmacy benefit manager KW - interoperability KW - adverse drug event KW - identity management KW - identifier KW - pharmacy KW - pharmaceuticals KW - drug N2 - Background: The United States, unlike other high-income countries, currently has no national unique patient identifier to facilitate health information exchange. Because of security and privacy concerns, Congress, in 1998, prevented the government from promulgating a unique patient identifier. The Health and Human Services funding bill that was enacted in 2019 requires that Health and Human Services report their recommendations on patient identification to Congress. While there are anecdotes of incomplete health care data due to patient misidentification, to date there have been insufficient large-scale analyses measuring improvements to patient care that a unique patient identifier might provide. This lack of measurement has made it difficult for policymakers to balance security and privacy concerns against the value of potential improvements. Objective: We sought to determine the frequency of serious drug-drug interaction alerts discovered because a pharmacy benefits manager uses a universal patient identifier and estimate undiscovered serious drug-drug interactions because pharmacy benefit managers do not yet fully share patient records. Methods: We conducted a retrospective study of serious drug-drug interaction alerts provided from September 1, 2016 to August 31, 2019 to retail pharmacies by a national pharmacy benefit manager that uses a unique patient identifier. We compared each alert to the contributing prescription and determined whether the unique patient identifier was necessary in order to identify the crossover alert. We classified each alert?s disposition as override, abandonment, or replacement. Using the crossover alert rate and sample population size, we inferred a rate of missing serious drug-drug interaction alerts for the United States. We performed logistic regression in order to identify factors correlated with crossover and alert outcomes. Results: Among a population of 49.7 million patients, 242,646 serious drug-drug interaction alerts occurred in 3 years. Of these, 2388 (1.0%) crossed insurance and were discovered because the pharmacy benefit manager used a unique patient identifier. We estimate that up to 10% of serious drug-drug alerts in the United States go undetected by pharmacy benefit managers because of unexchanged information or pharmacy benefit managers that do not use a unique patient identifier. These information gaps may contribute, annually, to up to 6000 patients in the United States receiving a contraindicated medication. Conclusions: Comprehensive patient identification across disparate data sources can help protect patients from serious drug-drug interactions. To better safeguard patients, providers should (1) adopt a comprehensive patient identification strategy and (2) share patient prescription history to improve clinical decision support. UR - http://medinform.jmir.org/2020/11/e23353/ UR - http://dx.doi.org/10.2196/23353 UR - http://www.ncbi.nlm.nih.gov/pubmed/33216009 ID - info:doi/10.2196/23353 ER - TY - JOUR AU - Li, Xiaoying AU - Lin, Xin AU - Ren, Huiling AU - Guo, Jinjing PY - 2020/7/20 TI - Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study JO - J Med Internet Res SP - e20443 VL - 22 IS - 7 KW - ontology KW - adverse drug reactions KW - package inserts KW - information retrieval KW - natural language processing KW - bioinformatics KW - drug KW - adverse events KW - machine-understandable knowledge KW - clinical applications N2 - Background: Licensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. Objective: This paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. Methods: Based on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. Results: We collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. Conclusions: Ontological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications. UR - https://www.jmir.org/2020/7/e20443 UR - http://dx.doi.org/10.2196/20443 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706718 ID - info:doi/10.2196/20443 ER - TY - JOUR AU - Poly, Nasrin Tahmina AU - Islam, Md.Mohaimenul AU - Yang, Hsuan-Chia AU - Li, (Jack) Yu-Chuan PY - 2020/7/20 TI - Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review JO - JMIR Med Inform SP - e15653 VL - 8 IS - 7 KW - clinical decision system KW - computerized physician order entry KW - alert fatigue KW - override KW - patient safety N2 - Background: The clinical decision support system (CDSS) has become an indispensable tool for reducing medication errors and adverse drug events. However, numerous studies have reported that CDSS alerts are often overridden. The increase in override rates has raised questions about the appropriateness of CDSS application along with concerns about patient safety and quality of care. Objective: The aim of this study was to conduct a systematic review to examine the override rate, the reasons for the alert override at the time of prescribing, and evaluate the appropriateness of overrides. Methods: We searched electronic databases, including Google Scholar, PubMed, Embase, Scopus, and Web of Science, without language restrictions between January 1, 2000 and March 31, 2019. Two authors independently extracted data and crosschecked the extraction to avoid errors. The quality of the included studies was examined following Cochrane guidelines. Results: We included 23 articles in our systematic review. The range of average override alerts was 46.2%-96.2%. An average of 29.4%-100% of the overrides alerts were classified as appropriate, and the rate of appropriateness varied according to the alert type (drug-allergy interaction 63.4%-100%, drug-drug interaction 0%-95%, dose 43.9%-88.8%, geriatric 14.3%-57%, renal 27%-87.5%). The interrater reliability for the assessment of override alerts appropriateness was excellent (kappa=0.79-0.97). The most common reasons given for the override were ?will monitor? and ?patients have tolerated before.? Conclusions: The findings of our study show that alert override rates are high, and certain categories of overrides such as drug-drug interaction, renal, and geriatric were classified as inappropriate. Nevertheless, large proportions of drug duplication, drug-allergy, and formulary alerts were appropriate, suggesting that these groups of alerts can be primary targets to revise and update the system for reducing alert fatigue. Future efforts should also focus on optimizing alert types, providing clear information, and explaining the rationale of the alert so that essential alerts are not inappropriately overridden. UR - https://medinform.jmir.org/2020/7/e15653 UR - http://dx.doi.org/10.2196/15653 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706721 ID - info:doi/10.2196/15653 ER - TY - JOUR AU - Dandala, Bharath AU - Joopudi, Venkata AU - Tsou, Ching-Huei AU - Liang, J. Jennifer AU - Suryanarayanan, Parthasarathy PY - 2020/7/10 TI - Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models JO - JMIR Med Inform SP - e18417 VL - 8 IS - 7 KW - electronic health records KW - adverse drug events KW - natural language processing KW - deep learning KW - information extraction KW - adverse drug reaction reporting systems KW - named entity recognition KW - relation extraction N2 - Background: An adverse drug event (ADE) is commonly defined as ?an injury resulting from medical intervention related to a drug.? Providing information related to ADEs and alerting caregivers at the point of care can reduce the risk of prescription and diagnostic errors and improve health outcomes. ADEs captured in structured data in electronic health records (EHRs) as either coded problems or allergies are often incomplete, leading to underreporting. Therefore, it is important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain a richer documentation of a patient?s ADE. Several natural language processing (NLP) systems have been proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for the automatic extraction of ADEs from clinical notes. Objective: This study aims to improve the automatic extraction of ADEs and related information such as drugs, their attributes, and reason for administration from the clinical notes of patients. Methods: This research was conducted using discharge summaries from the Medical Information Mart for Intensive Care III (MIMIC-III) database obtained through the 2018 National NLP Clinical Challenges (n2c2) annotated with drugs, drug attributes (ie, strength, form, frequency, route, dosage, duration), ADEs, reasons, and relations between drugs and other entities. We developed a deep learning?based system for extracting these drug-centric concepts and relations simultaneously using a joint method enhanced with contextualized embeddings, a position-attention mechanism, and knowledge representations. The joint method generated different sentence representations for each drug, which were then used to extract related concepts and relations simultaneously. Contextualized representations trained on the MIMIC-III database were used to capture context-sensitive meanings of words. The position-attention mechanism amplified the benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the US Food and Drug Administration Adverse Event Reporting System database to improve relation extraction, especially when contextual clues were insufficient. Results: Our system achieved new state-of-the-art results on the n2c2 data set, with significant improvements in recognizing crucial drug?reason (F1=0.650 versus F1=0.579) and drug?ADE (F1=0.490 versus F1=0.476) relations. Conclusions: This study presents a system for extracting drug-centric concepts and relations that outperformed current state-of-the-art results and shows that contextualized embeddings, position-attention mechanisms, and knowledge graph embeddings effectively improve deep learning?based concepts and relation extraction. This study demonstrates the potential for deep learning?based methods to help extract real-world evidence from unstructured patient data for drug safety surveillance. UR - https://medinform.jmir.org/2020/7/e18417 UR - http://dx.doi.org/10.2196/18417 UR - http://www.ncbi.nlm.nih.gov/pubmed/32459650 ID - info:doi/10.2196/18417 ER - TY - JOUR AU - Black, C. Joshua AU - Margolin, R. Zachary AU - Olson, A. Richard AU - Dart, C. Richard PY - 2020/6/29 TI - Online Conversation Monitoring to Understand the Opioid Epidemic: Epidemiological Surveillance Study JO - JMIR Public Health Surveill SP - e17073 VL - 6 IS - 2 KW - epidemiological surveillance KW - infoveillance KW - infodemiology KW - opioids KW - social media KW - misuse KW - abuse KW - addiction KW - overdose KW - death N2 - Background: Between 2016 and 2017, the national mortality rate involving opioids continued its escalation; opioid deaths rose from 42,249 to 47,600, bringing the public health crisis to a new height. Considering that 69% of adults in the United States use online social media sites, a resource that builds a more complete understanding of prescription drug misuse and abuse could supplement traditional surveillance instruments. The Food and Drug Administration has identified 5 key risks and consequences of opioid drugs?misuse, abuse, addiction, overdose, and death. Identifying posts that discuss these key risks could lead to novel information that is not typically captured by traditional surveillance systems. Objective: The goal of this study was to describe the trends of online posts (frequency over time) involving abuse, misuse, addiction, overdose, and death in the United States and to describe the types of websites that host these discussions. Internet posts that mentioned fentanyl, hydrocodone, oxycodone, or oxymorphone were examined. Methods: Posts that did not refer to personal experiences were removed, after which 3.1 million posts remained. A stratified sample of 61,000 was selected. Unstructured data were classified into 5 key risks by manually coding for key outcomes of misuse, abuse, addiction, overdose, and death. Sampling probabilities of the coded posts were used to estimate the total post volume for each key risk. Results: Addiction and misuse were the two most commonly discussed key risks for hydrocodone, oxycodone, and oxymorphone. For fentanyl, overdose and death were the most discussed key risks. Fentanyl had the highest estimated number of misuse-, overdose-, and death-related mentions (41,808, 42,659, and 94,169, respectively). Oxycodone had the highest estimated number of abuse- and addiction-related mentions (3548 and 12,679, respectively). The estimated volume of online posts for fentanyl increased by more than 10-fold in late 2017 and 2018. The odds of discussing fentanyl overdose (odds ratios [OR] 4.32, 95% CI 2.43-7.66) and death (OR 5.05, 95% CI 3.10-8.21) were higher for social media, while the odds of discussing fentanyl abuse (OR 0.10, 95% CI 0.04-0.22) and addiction (OR 0.24, 95% CI 0.15-0.38) were higher for blogs and forums. Conclusions: Of the 5 FDA-defined key risks, fentanyl overdose and death has dominated discussion in recent years, while discussion of oxycodone, hydrocodone, and oxymorphone has decreased. As drug-related deaths continue to increase, an understanding of the motivations, circumstances, and consequences of drug abuse would assist in developing policy responses. Furthermore, content was notably different based on media origin, and studies that exclusively use either social media sites (such as Twitter) or blogs and forums could miss important content. This study sets out sustainable, ongoing methodology for surveilling internet postings regarding these drugs. UR - http://publichealth.jmir.org/2020/2/e17073/ UR - http://dx.doi.org/10.2196/17073 UR - http://www.ncbi.nlm.nih.gov/pubmed/32597786 ID - info:doi/10.2196/17073 ER - TY - JOUR AU - Yu, Yue AU - Ruddy, Kathryn AU - Mansfield, Aaron AU - Zong, Nansu AU - Wen, Andrew AU - Tsuji, Shintaro AU - Huang, Ming AU - Liu, Hongfang AU - Shah, Nilay AU - Jiang, Guoqian PY - 2020/6/12 TI - Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study JO - JMIR Med Inform SP - e17353 VL - 8 IS - 6 KW - immunotherapy/adverse effects KW - drug-related side effects and adverse reactions KW - pharmacovigilance KW - adverse drug reaction reporting systems/standards KW - text mining N2 - Background: Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. Objective: The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration?approved immune checkpoint inhibitors. Methods: In our framework, we first used the Food and Drug Administration?s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals. Results: By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals. Conclusions: We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection. UR - http://medinform.jmir.org/2020/6/e17353/ UR - http://dx.doi.org/10.2196/17353 UR - http://www.ncbi.nlm.nih.gov/pubmed/32530430 ID - info:doi/10.2196/17353 ER - TY - JOUR AU - Liu, Zhike AU - Zhang, Liang AU - Yang, Yu AU - Meng, Ruogu AU - Fang, Ting AU - Dong, Ying AU - Li, Ning AU - Xu, Guozhang AU - Zhan, Siyan PY - 2020/6/1 TI - Active Surveillance of Adverse Events Following Human Papillomavirus Vaccination: Feasibility Pilot Study Based on the Regional Health Care Information Platform in the City of Ningbo, China JO - J Med Internet Res SP - e17446 VL - 22 IS - 6 KW - safety KW - HPV KW - human papillomavirus KW - vaccine KW - active surveillance N2 - Background: Comprehensive safety data for vaccines from post-licensure surveillance, especially active surveillance, could guide administrations and individuals to make reasonable decisions on vaccination. Therefore, we designed a pilot study to assess the capability of a regional health care information platform to actively monitor the safety of a newly licensed vaccine. Objective: This study aimed to conduct active surveillance of human papillomavirus (HPV) vaccine safety based on this information platform. Methods: In 2017, one of China?s most mature information platforms with superior data linkage was selected. A structured questionnaire and open-ended interview guidelines were developed to investigate the feasibility of active surveillance following HPV vaccination using the regional health care information platform in Ningbo. The questionnaire was sent to participants via email, and a face-to-face interview was conducted to confirm details or resolve discrepancies. Results: Five databases that could be considered essential to active surveillance of vaccine safety were integrated into the platform starting in 2015. Except for residents' health records, which had a coverage rate of 87%, the data sources covered more than 95% of the records that were documented in Ningbo. All the data could be inherently linked using the national identity card. There were 19,328 women who received the HPV vaccine, and 37,988 doses were administered in 2017 and 2018. Women aged 30-40 years accounted for the largest proportion. Quadrivalent vaccination accounted for 73.1% of total vaccination, a much higher proportion than that of bivalent vaccination. Of the first doses, 60 (60/19,328, 0.31%) occurred outside Ningbo. There were no missing data for vaccination-relevant variables, such as identity card, vaccine name, vaccination doses, vaccination date, and manufacturer. ICD-10 coding could be used to identify 9,180 cases using a predefined list of the outcomes of interest, and 1.88% of these cases were missing the identity card. During the 90 days following HPV vaccination, 4 incident cases were found through the linked vaccination history and electronic medical records. The combined incident rate of rheumatoid arthritis, optic neuritis, and Henoch-Schonlein purpura was 8.84/100,000 doses of bivalent HPV, and the incidence rate of rheumatoid arthritis was 3.75/100,000 doses of quadrivalent HPV. Conclusions: This study presents an available approach to initiate an active surveillance system for adverse events following HPV vaccination, based on a regional health care information platform in China. An extended observation period or the inclusion of additional functional sites is warranted to conduct future hypothesis-generating and hypothesis-confirming studies for vaccine safety concerns. UR - https://www.jmir.org/2020/6/e17446 UR - http://dx.doi.org/10.2196/17446 UR - http://www.ncbi.nlm.nih.gov/pubmed/32234696 ID - info:doi/10.2196/17446 ER - TY - JOUR AU - Slovis, H. Benjamin AU - Kairys, John AU - Babula, Bracken AU - Girondo, Melanie AU - Martino, Cara AU - Roke, M. Lindsey AU - Riggio, Jeffrey PY - 2020/3/31 TI - Discrepancies in Written Versus Calculated Durations in Opioid Prescriptions: Pre-Post Study JO - JMIR Med Inform SP - e16199 VL - 8 IS - 3 KW - informatics KW - electronic health record KW - opioids KW - prescription KW - duration N2 - Background: The United States is in the midst of an opioid epidemic. Long-term use of opioid medications is associated with an increased risk of dependence. The US Centers for Disease Control and Prevention makes specific recommendations regarding opioid prescribing, including that prescription quantities should not exceed the intended duration of treatment. Objective: The purpose of this study was to determine if opioid prescription quantities written at our institution exceed intended duration of treatment and whether enhancements to our electronic health record system improved any discrepancies. Methods: We examined the opioid prescriptions written at our institution for a 22-month period. We examined the duration of treatment documented in the prescription itself and calculated a duration based on the quantity of tablets and doses per day. We determined whether requiring documentation of the prescription duration affected these outcomes. Results: We reviewed 72,314 opioid prescriptions, of which 16.96% had a calculated duration that was greater than what was documented in the prescription. Making the duration a required field significantly reduced this discrepancy (17.95% vs 16.21%, P<.001) but did not eliminate it. Conclusions: Health information technology vendors should develop tools that, by default, accurately represent prescription durations and/or modify doses and quantities dispensed based on provider-entered durations. This would potentially reduce unintended prolonged opioid use and reduce the potential for long-term dependence. UR - https://medinform.jmir.org/2020/3/e16199 UR - http://dx.doi.org/10.2196/16199 UR - http://www.ncbi.nlm.nih.gov/pubmed/32229472 ID - info:doi/10.2196/16199 ER - TY - JOUR AU - Park, Rang Yu AU - Koo, HaYeong AU - Yoon, Young-Kwang AU - Park, Sumi AU - Lim, Young-Suk AU - Baek, Seunghee AU - Kim, Reong Hae AU - Kim, Won Tae PY - 2020/2/27 TI - Expedited Safety Reporting Through an Alert System for Clinical Trial Management at an Academic Medical Center: Retrospective Design Study JO - JMIR Med Inform SP - e14379 VL - 8 IS - 2 KW - clinical trial KW - adverse event KW - early detection KW - patient safety N2 - Background: Early detection or notification of adverse event (AE) occurrences during clinical trials is essential to ensure patient safety. Clinical trials take advantage of innovative strategies, clinical designs, and state-of-the-art technologies to evaluate efficacy and safety, however, early awareness of AE occurrences by investigators still needs to be systematically improved. Objective: This study aimed to build a system to promptly inform investigators when clinical trial participants make unscheduled visits to the emergency room or other departments within the hospital. Methods: We developed the Adverse Event Awareness System (AEAS), which promptly informs investigators and study coordinators of AE occurrences by automatically sending text messages when study participants make unscheduled visits to the emergency department or other clinics at our center. We established the AEAS in July 2015 in the clinical trial management system. We compared the AE reporting timeline data of 305 AE occurrences from 74 clinical trials between the preinitiative period (December 2014-June 2015) and the postinitiative period (July 2015-June 2016) in terms of three AE awareness performance indicators: onset to awareness, awareness to reporting, and onset to reporting. Results: A total of 305 initial AE reports from 74 clinical trials were included. All three AE awareness performance indicators were significantly lower in the postinitiative period. Specifically, the onset-to-reporting times were significantly shorter in the postinitiative period (median 1 day [IQR 0-1], mean rank 140.04 [SD 75.35]) than in the preinitiative period (median 1 day [IQR 0-4], mean rank 173.82 [SD 91.07], P?.001). In the phase subgroup analysis, the awareness-to-reporting and onset-to-reporting indicators of phase 1 studies were significantly lower in the postinitiative than in the preinitiative period (preinitiative: median 1 day, mean rank of awareness to reporting 47.94, vs postinitiative: median 0 days, mean rank of awareness to reporting 35.75, P=.01; and preinitiative: median 1 day, mean rank of onset to reporting 47.4, vs postinitiative: median 1 day, mean rank of onset to reporting 35.99, P=.03). The risk-level subgroup analysis found that the onset-to-reporting time for low- and high-risk studies significantly decreased postinitiative (preinitiative: median 4 days, mean rank of low-risk studies 18.73, vs postinitiative: median 1 day, mean rank of low-risk studies 11.76, P=.02; and preinitiative: median 1 day, mean rank of high-risk studies 117.36, vs postinitiative: median 1 day, mean rank of high-risk studies 97.27, P=.01). In particular, onset to reporting was reduced more in the low-risk trial than in the high-risk trial (low-risk: median 4-0 days, vs high-risk: median 1-1 day). Conclusions: We demonstrated that a real-time automatic alert system can effectively improve safety reporting timelines. The improvements were prominent in phase 1 and in low- and high-risk clinical trials. These findings suggest that an information technology-driven automatic alert system effectively improves safety reporting timelines, which may enhance patient safety. UR - http://medinform.jmir.org/2020/2/e14379/ UR - http://dx.doi.org/10.2196/14379 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/14379 ER - TY - JOUR AU - Timimi, Farris AU - Ray, Sara AU - Jones, Erik AU - Aase, Lee AU - Hoffman, Kathleen PY - 2019/11/28 TI - Patient-Reported Outcomes in Online Communications on Statins, Memory, and Cognition: Qualitative Analysis Using Online Communities JO - J Med Internet Res SP - e14809 VL - 21 IS - 11 KW - social media KW - hydroxymethylglutaryl-CoA reductase inhibitors KW - drug-related side effects and adverse reactions KW - memory loss KW - PROMs KW - pharmacovigilance KW - infodemiology KW - infoveillance KW - peer-support groups N2 - Background: In drug development clinical trials, there is a need for balance between restricting variables by setting eligibility criteria and representing the broader patient population that may use a product once it is approved. Similarly, although recent policy initiatives focusing on the inclusion of historically underrepresented groups are being implemented, barriers still remain. These limitations of clinical trials may mask potential product benefits and side effects. To bridge these gaps, online communication in health communities may serve as an additional population signal for drug side effects. Objective: The aim of this study was to employ a nontraditional dataset to identify drug side-effect signals. The study was designed to apply both natural language processing (NLP) technology and hands-on linguistic analysis to a set of online posts from known statin users to (1) identify any underlying crossover between the use of statins and impairment of memory or cognition and (2) obtain patient lexicon in their descriptions of experiences with statin medications and memory changes. Methods: Researchers utilized user-generated content on Inspire, looking at over 11 million posts across Inspire. Posts were written by patients and caregivers belonging to a variety of communities on Inspire. After identifying these posts, researchers used NLP and hands-on linguistic analysis to draw and expand upon correlations among statin use, memory, and cognition. Results: NLP analysis of posts identified statistical correlations between statin users and the discussion of memory impairment, which were not observed in control groups. NLP found that, out of all members on Inspire, 3.1% had posted about memory or cognition. In a control group of those who had posted about TNF inhibitors, 6.2% had also posted about memory and cognition. In comparison, of all those who had posted about a statin medication, 22.6% (P<.001) also posted about memory and cognition. Furthermore, linguistic analysis of a sample of posts provided themes and context to these statistical findings. By looking at posts from statin users about memory, four key themes were found and described in detail in the data: memory loss, aphasia, cognitive impairment, and emotional change. Conclusions: Correlations from this study point to a need for further research on the impact of statins on memory and cognition. Furthermore, when using nontraditional datasets, such as online communities, NLP and linguistic methodologies broaden the population for identifying side-effect signals. For side effects such as those on memory and cognition, where self-reporting may be unreliable, these methods can provide another avenue to inform patients, providers, and the Food and Drug Administration. UR - http://www.jmir.org/2019/11/e14809/ UR - http://dx.doi.org/10.2196/14809 UR - http://www.ncbi.nlm.nih.gov/pubmed/31778117 ID - info:doi/10.2196/14809 ER - TY - JOUR AU - Borchert, S. Jill AU - Wang, Bo AU - Ramzanali, Muzaina AU - Stein, B. Amy AU - Malaiyandi, M. Latha AU - Dineley, E. Kirk PY - 2019/11/8 TI - Adverse Events Due to Insomnia Drugs Reported in a Regulatory Database and Online Patient Reviews: Comparative Study JO - J Med Internet Res SP - e13371 VL - 21 IS - 11 KW - drug safety KW - drug ineffective KW - postmarketing KW - pharmacovigilance KW - internet KW - pharmacoepidemiology KW - adverse effect KW - hypnotic KW - insomnia KW - patient-reported outcomes N2 - Background: Patient online drug reviews are a resource for other patients seeking information about the practical benefits and drawbacks of drug therapies. Patient reviews may also serve as a source of postmarketing safety data that are more user-friendly than regulatory databases. However, the reliability of online reviews has been questioned, because they do not undergo professional review and lack means of verification. Objective: We evaluated online reviews of hypnotic medications, because they are commonly used and their therapeutic efficacy is particularly amenable to patient self-evaluation. Our primary objective was to compare the types and frequencies of adverse events reported to the Food and Drug Administration Adverse Event Reporting System (FAERS) with analogous information in patient reviews on the consumer health website Drugs.com. The secondary objectives were to describe patient reports of efficacy and adverse events and assess the influence of medication cost, effectiveness, and adverse events on user ratings of hypnotic medications. Methods: Patient ratings and narratives were retrieved from 1407 reviews on Drugs.com between February 2007 and March 2018 for eszopiclone, ramelteon, suvorexant, zaleplon, and zolpidem. Reviews were coded to preferred terms in the Medical Dictionary for Regulatory Activities. These reviews were compared to 5916 cases in the FAERS database from January 2015 to September 2017. Results: Similar adverse events were reported to both Drugs.com and FAERS. Both resources identified a lack of efficacy as a common complaint for all five drugs. Both resources revealed that amnesia commonly occurs with eszopiclone, zaleplon, and zolpidem, while nightmares commonly occur with suvorexant. Compared to FAERS, online reviews of zolpidem reported a much higher frequency of amnesia and partial sleep activities. User ratings were highest for zolpidem and lowest for suvorexant. Statistical analyses showed that patient ratings are influenced by considerations of efficacy and adverse events, while drug cost is unimportant. Conclusions: For hypnotic medications, online patient reviews and FAERS emphasized similar adverse events. Online reviewers rated drugs based on perception of efficacy and adverse events. We conclude that online patient reviews of hypnotics are a valid source that can supplement traditional adverse event reporting systems. UR - http://www.jmir.org/2019/11/e13371/ UR - http://dx.doi.org/10.2196/13371 UR - http://www.ncbi.nlm.nih.gov/pubmed/31702558 ID - info:doi/10.2196/13371 ER - TY - JOUR AU - Black, Curtis Joshua AU - Rockhill, Karilynn AU - Forber, Alyssa AU - Amioka, Elise AU - May, Patrick K. AU - Haynes, M. Colleen AU - Dasgupta, Nabarun AU - Dart, C. Richard PY - 2019/10/25 TI - An Online Survey for Pharmacoepidemiological Investigation (Survey of Non-Medical Use of Prescription Drugs Program): Validation Study JO - J Med Internet Res SP - e15830 VL - 21 IS - 10 KW - nonprobability methods KW - general population survey KW - drug abuse KW - calibration weights N2 - Background: In rapidly changing fields such as the study of drug use, the need for accurate and timely data is paramount to properly inform policy and intervention decisions. Trends in drug use can change rapidly by month, and using study designs with flexible modules could present advantages. Timely data from online panels can inform proactive interventions against emerging trends, leading to a faster public response. However, threats to validity from using online panels must be addressed to create accurate estimates. Objective: The objective of this study was to demonstrate a comprehensive methodological approach that optimizes a nonprobability, online opt-in sample to provide timely, accurate national estimates on prevalence of drug use. Methods: The Survey of Non-Medical Use of Prescription Drugs Program from the Researched Abuse, Diversion and Addiction Related Surveillance (RADARS) System is an online, cross-sectional survey on drug use in the United States, and several best practices were implemented. To optimize final estimates, two best practices were investigated in detail: exclusion of respondents showing careless or improbable responding patterns and calibration of weights. The approach in this work was to cumulatively implement each method, which improved key estimates during the third quarter 2018 survey launch. Cutoffs for five exclusion criteria were tested. Using a series of benchmarks, average relative bias and changes in bias were calculated for 33 different weighting variable combinations. Results: There were 148,274 invitations sent to panelists, with 40,021 who initiated the survey (26.99%). After eligibility assessment, 20.23% (29,998/148,274) of the completed questionnaires were available for analysis. A total of 0.52% (157/29,998) of respondents were excluded based on careless or improbable responses; however, these exclusions had larger impacts on lower volume drugs. Number of exclusions applied were negatively correlated to total dispensing volume by drug (Spearman ?=?.88, P<.001). A weighting scheme including three demographic and two health characteristics reduced average relative bias by 31.2%. After weighting, estimates of drug use decreased, reflecting a weighted sample that had healthier benchmarks than the unweighted sample. Conclusions: Our study illustrates a new approach to using nonprobability online panels to achieve national prevalence estimates for drug abuse. We were able to overcome challenges with using nonprobability internet samples, including misclassification due to improbable responses. Final drug use and health estimates demonstrated concurrent validity to national probability-based drug use and health surveys. Inclusion of multiple best practices cumulatively improved the estimates generated. This method can bridge the information gap when there is a need for prompt, accurate national data. UR - http://www.jmir.org/2019/10/e15830/ UR - http://dx.doi.org/10.2196/15830 UR - http://www.ncbi.nlm.nih.gov/pubmed/31654568 ID - info:doi/10.2196/15830 ER - TY - JOUR AU - Munnoch, Sally-Anne AU - Cashman, Patrick AU - Peel, Roseanne AU - Attia, John AU - Hure, Alexis AU - Durrheim, N. David PY - 2019/10/23 TI - Participant-Centered Online Active Surveillance for Adverse Events Following Vaccination in a Large Clinical Trial: Feasibility and Usability Study JO - J Med Internet Res SP - e14791 VL - 21 IS - 10 KW - clinical trials KW - active surveillance KW - adverse events following immunization KW - technology KW - vaccination N2 - Background: Active participant monitoring of adverse events following immunization (AEFI) is a recent development to improve the speed and transparency of vaccine safety postmarketing. Vaxtracker, an online tool used to monitor vaccine safety, has successfully demonstrated its usefulness in postmarketing surveillance of newly introduced childhood vaccines. However, its use in older participants, or for monitoring patients participating in large clinical trials, has not been evaluated. Objective: The objective of this study was to monitor AEFIs in older participants enrolled in the Australian Study for the Prevention through the Immunisation of Cardiovascular Events (AUSPICE) trial, and to evaluate the usefulness and effectiveness of Vaxtracker in this research setting. Methods: AUSPICE is a multicenter, randomized, placebo-controlled, double-blinded trial in which participants aged 55 to 61 years were given either the pneumococcal polysaccharide vaccine (23vPPV) or 0.9% saline placebo. Vaxtracker was used to monitor AEFIs in participants in either treatment arm through the administration of two online questionnaires. A link to each questionnaire was sent to participants via email or short message service (SMS) text message 7 and 28 days following vaccination. Data were collated and analyzed in near-real time to identify any possible safety signals indicating problems with the vaccine or placebo. Results: All 4725 AUSPICE participants were enrolled in Vaxtracker. Participant response rates for the first and final survey were 96.47% (n=4558) and 96.65% (n=4525), respectively. The online survey was completed by 90.23% (4083/4525) of Vaxtracker participants within 3 days of receiving the link. AEFIs were reported by 34.40% (805/2340) of 23vPPV recipients and 10.29% (240/2332) of placebo recipients in the 7 days following vaccination. Dominant symptoms for vaccine and placebo recipients were pain at the injection site (587/2340, 25.09%) and fatigue (103/2332, 4.42%), respectively. Females were more likely to report symptoms following vaccination with 23vPPV compared with males (433/1138, 38.05% versus 372/1202, 30.95%; P<.001). Conclusions: Vaxtracker is an effective tool for monitoring AEFIs in the 55 to 61 years age group. Participant response rates were high for both surveys, in both treatment arms and for each method of sending the survey. This study indicates that administration of 23vPPV was well-tolerated in this cohort. Vaxtracker has successfully demonstrated its application in the monitoring of adverse events in near-real time following vaccination in people participating in a national clinical trial. Trial Registration: Australian New Zealand Trial Registry Number (ACTRN) 12615000536561; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368506 UR - https://www.jmir.org/2019/10/e14791 UR - http://dx.doi.org/10.2196/14791 UR - http://www.ncbi.nlm.nih.gov/pubmed/31647470 ID - info:doi/10.2196/14791 ER - TY - JOUR AU - Golder, Su AU - Scantlebury, Arabella AU - Christmas, Helen PY - 2019/08/29 TI - Understanding Public Attitudes Toward Researchers Using Social Media for Detecting and Monitoring Adverse Events Data: Multi Methods Study JO - J Med Internet Res SP - e7081 VL - 21 IS - 8 KW - adverse effects KW - social media KW - ethics KW - research KW - qualitative research KW - digital health KW - infodemiology KW - infoveillance KW - pharmacovigilance KW - surveillance N2 - Background: Adverse events are underreported in research studies, particularly randomized controlled trials and pharmacovigilance studies. A method that researchers could use to identify more complete safety profiles for medications is to use social media analytics. However, patient?s perspectives on the ethical issues associated with using patient reports of adverse drug events on social media are unclear. Objective: The objective of this study was to explore the ethics of using social media for detecting and monitoring adverse events for research purposes using a multi methods approach. Methods: A multi methods design comprising qualitative semistructured interviews (n=24), a focus group (n=3), and 3 Web-based discussions (n=20) with members of the public was adopted. Findings from a recent systematic review on the use of social media for monitoring adverse events provided a theoretical framework to interpret the study?s findings. Results: Views were ascertained regarding the potential benefits and harms of the research, privacy expectations, informed consent, and social media platform. Although the majority of participants were supportive of social media content being used for research on adverse events, a small number of participants strongly opposed the idea. The potential benefit of the research was cited as the most influential factor to whether participants would give their consent to their data being used for research. There were also some caveats to people?s support for the use of their social media data for research purposes: the type of social media platform and consideration of the vulnerability of the social media user. Informed consent was regarded as difficult to obtain and this divided the opinion on whether it should be sought. Conclusions: Social media users were generally positive about their social media data being used for research purposes; particularly for research on adverse events. However, approval was dependent on the potential benefit of the research and that individuals are protected from harm. Further study is required to establish when consent is required for an individual?s social media data to be used. UR - http://www.jmir.org/2019/8/e7081/ UR - http://dx.doi.org/10.2196/jmir.7081 UR - http://www.ncbi.nlm.nih.gov/pubmed/31469079 ID - info:doi/10.2196/jmir.7081 ER - TY - JOUR AU - Nikfarjam, Azadeh AU - Ransohoff, D. Julia AU - Callahan, Alison AU - Jones, Erik AU - Loew, Brian AU - Kwong, Y. Bernice AU - Sarin, Y. Kavita AU - Shah, H. Nigam PY - 2019/06/03 TI - Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection JO - JMIR Public Health Surveill SP - e11264 VL - 5 IS - 2 KW - natural language processing KW - signal detection KW - adverse drug reactions KW - social media KW - drug-related side effects KW - medical oncology KW - antineoplastic agents KW - machine learning N2 - Background: Adverse drug reactions (ADRs) occur in nearly all patients on chemotherapy, causing morbidity and therapy disruptions. Detection of such ADRs is limited in clinical trials, which are underpowered to detect rare events. Early recognition of ADRs in the postmarketing phase could substantially reduce morbidity and decrease societal costs. Internet community health forums provide a mechanism for individuals to discuss real-time health concerns and can enable computational detection of ADRs. Objective: The goal of this study is to identify cutaneous ADR signals in social health networks and compare the frequency and timing of these ADRs to clinical reports in the literature. Methods: We present a natural language processing-based, ADR signal-generation pipeline based on patient posts on Internet social health networks. We identified user posts from the Inspire health forums related to two chemotherapy classes: erlotinib, an epidermal growth factor receptor inhibitor, and nivolumab and pembrolizumab, immune checkpoint inhibitors. We extracted mentions of ADRs from unstructured content of patient posts. We then performed population-level association analyses and time-to-detection analyses. Results: Our system detected cutaneous ADRs from patient reports with high precision (0.90) and at frequencies comparable to those documented in the literature but an average of 7 months ahead of their literature reporting. Known ADRs were associated with higher proportional reporting ratios compared to negative controls, demonstrating the robustness of our analyses. Our named entity recognition system achieved a 0.738 microaveraged F-measure in detecting ADR entities, not limited to cutaneous ADRs, in health forum posts. Additionally, we discovered the novel ADR of hypohidrosis reported by 23 patients in erlotinib-related posts; this ADR was absent from 15 years of literature on this medication and we recently reported the finding in a clinical oncology journal. Conclusions: Several hundred million patients report health concerns in social health networks, yet this information is markedly underutilized for pharmacosurveillance. We demonstrated the ability of a natural language processing-based signal-generation pipeline to accurately detect patient reports of ADRs months in advance of literature reporting and the robustness of statistical analyses to validate system detections. Our findings suggest the important contributions that social health network data can play in contributing to more comprehensive and timely pharmacovigilance. UR - http://publichealth.jmir.org/2019/2/e11264/ UR - http://dx.doi.org/10.2196/11264 UR - http://www.ncbi.nlm.nih.gov/pubmed/31162134 ID - info:doi/10.2196/11264 ER - TY - JOUR AU - Morath, Benedict AU - Wien, Katharina AU - Hoppe-Tichy, Torsten AU - Haefeli, Emil Walter AU - Seidling, Marita Hanna PY - 2019/04/08 TI - Structure and Content of Drug Monitoring Advices Included in Discharge Letters at Interfaces of Care: Exploratory Analysis Preceding Database Development JO - JMIR Med Inform SP - e10832 VL - 7 IS - 2 KW - drug monitoring KW - patient discharge summaries KW - transition of care N2 - Background: Inadequate drug monitoring of drug therapy after hospital discharge facilitates adverse drug events and preventable hospital readmissions. Objective: This study aimed to analyze the structure and content of drug monitoring advices of a representative sample of discharge letters as a basis for future electronic information systems. Methods: On 2 days in November 2016, all discharge letters of 3 departments of a university hospital were extracted from the hospital information system. The frequency, content, and structure of drug monitoring advices in discharge letters were investigated and compared with the theoretical monitoring requirements expressed in the corresponding summaries of product characteristics (SmPC). The quality of the drug monitoring advices in the discharge letters was rated with the domains of an adapted systematic instructions for monitoring (SIM) score. Results: In total, 154 discharge letters were analyzed containing 1180 brands (240 active pharmaceutical substances), of which 50.42% (595/1180) could theoretically be amended with a monitoring advice according to the SmPC. In reality, 40 discharge letters (26.0%, 40/154) contained a total of 66 monitoring advices for 57 brands (4.83%, 57/1180), comprising 18 different monitoring parameters. Drug monitoring advices only addressed mean 1.9 (SD 0.8) of the 7 domains of the SIM score and frequently did not address reasons for monitoring (86%, 57/66), the timing of monitoring, that is, the start (76%, 50/66), the frequency (94%, 63/66), the stop (95%, 63/66), and how to react (83%, 55/66). Conclusions: Drug monitoring advices were mostly absent in discharge letters and a gold standard for appropriate drug monitoring advices was lacking. Hence, more effort should be put in the development of tools that facilitate easy presentation of clinically meaningful drug monitoring advices at the point of care. UR - https://medinform.jmir.org/2019/2/e10832/ UR - http://dx.doi.org/10.2196/10832 UR - http://www.ncbi.nlm.nih.gov/pubmed/30958278 ID - info:doi/10.2196/10832 ER - TY - JOUR AU - Wang, Chi-Shiang AU - Lin, Pei-Ju AU - Cheng, Ching-Lan AU - Tai, Shu-Hua AU - Kao Yang, Yea-Huei AU - Chiang, Jung-Hsien PY - 2019/02/06 TI - Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model JO - J Med Internet Res SP - e11016 VL - 21 IS - 2 KW - adverse drug reactions KW - deep neural network KW - drug representation KW - machine learning KW - pharmacovigilance N2 - Background: Adverse drug reactions (ADRs) are common and are the underlying cause of over a million serious injuries and deaths each year. The most familiar method to detect ADRs is relying on spontaneous reports. Unfortunately, the low reporting rate of spontaneous reports is a serious limitation of pharmacovigilance. Objective: The objective of this study was to identify a method to detect potential ADRs of drugs automatically using a deep neural network (DNN). Methods: We designed a DNN model that utilizes the chemical, biological, and biomedical information of drugs to detect ADRs. This model aimed to fulfill two main purposes: identifying the potential ADRs of drugs and predicting the possible ADRs of a new drug. For improving the detection performance, we distributed representations of the target drugs in a vector space to capture the drug relationships using the word-embedding approach to process substantial biomedical literature. Moreover, we built a mapping function to address new drugs that do not appear in the dataset. Results: Using the drug information and the ADRs reported up to 2009, we predicted the ADRs of drugs recorded up to 2012. There were 746 drugs and 232 new drugs, which were only recorded in 2012 with 1325 ADRs. The experimental results showed that the overall performance of our model with mean average precision at top-10 achieved is 0.523 and the rea under the receiver operating characteristic curve (AUC) score achieved is 0.844 for ADR prediction on the dataset. Conclusions: Our model is effective in identifying the potential ADRs of a drug and the possible ADRs of a new drug. Most importantly, it can detect potential ADRs irrespective of whether they have been reported in the past. UR - http://www.jmir.org/2019/2/e11016/ UR - http://dx.doi.org/10.2196/11016 UR - http://www.ncbi.nlm.nih.gov/pubmed/30724742 ID - info:doi/10.2196/11016 ER - TY - JOUR AU - Yang, Cheng-Yi AU - Chen, Ray-Jade AU - Chou, Wan-Lin AU - Lee, Yuarn-Jang AU - Lo, Yu-Sheng PY - 2019/02/01 TI - An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation JO - J Med Internet Res SP - e12341 VL - 21 IS - 2 KW - influenza KW - epidemics KW - influenza surveillance KW - electronic disease surveillance KW - electronic medical records KW - electronic health records KW - public health N2 - Background: Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. Objective: We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. Methods: First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. Results: Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables?TMUHcS-RITP and TMUHcS-IMU?showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. Conclusions: Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations. UR - http://www.jmir.org/2019/2/e12341/ UR - http://dx.doi.org/10.2196/12341 UR - http://www.ncbi.nlm.nih.gov/pubmed/30707099 ID - info:doi/10.2196/12341 ER - TY - JOUR AU - Flanagan, M. James AU - Skrobanski, Hanna AU - Shi, Xin AU - Hirst, Yasemin PY - 2019/01/17 TI - Self-Care Behaviors of Ovarian Cancer Patients Before Their Diagnosis: Proof-of-Concept Study JO - JMIR Cancer SP - e10447 VL - 5 IS - 1 KW - cancer KW - early diagnosis KW - proof of concept KW - focus group KW - acceptability KW - data linkage KW - cancer surveillance N2 - Background: Longer patient intervals can lead to more late-stage cancer diagnoses and higher mortality rates. Individuals may delay presenting to primary care with red flag symptoms and instead turn to the internet to seek information, purchase over-the-counter medication, and change their diet or exercise habits. With advancements in machine learning, there is the potential to explore this complex relationship between a patient?s symptom appraisal and their first consultation at primary care through linkage of existing datasets (eg, health, commercial, and online). Objective: Here, we aimed to explore feasibility and acceptability of symptom appraisal using commercial- and health-data linkages for cancer symptom surveillance. Methods: A proof-of-concept study was developed to assess the general public?s acceptability of commercial- and health-data linkages for cancer symptom surveillance using a qualitative focus group study. We also investigated self-care behaviors of ovarian cancer patients using high-street retailer data, pre- and postdiagnosis. Results: Using a high-street retailer?s data, 1118 purchases?from April 2013 to July 2017?by 11 ovarian cancer patients and one healthy individual were analyzed. There was a unique presence of purchases for pain and indigestion medication prior to cancer diagnosis, which could signal disease in a larger sample. Qualitative findings suggest that the public are willing to consent to commercial- and health-data linkages as long as their data are safeguarded and users of this data are transparent about their purposes. Conclusions: Cancer symptom surveillance using commercial data is feasible and was found to be acceptable. To test efficacy of cancer surveillance using commercial data, larger studies are needed with links to individual electronic health records. UR - https://cancer.jmir.org/2019/1/e10447/ UR - http://dx.doi.org/10.2196/10447 UR - http://www.ncbi.nlm.nih.gov/pubmed/30664464 ID - info:doi/10.2196/10447 ER - TY - JOUR AU - Lancaster, Karla AU - Abuzour, Aseel AU - Khaira, Manmeet AU - Mathers, Annalise AU - Chan, April AU - Bui, Vivian AU - Lok, Annie AU - Thabane, Lehana AU - Dolovich, Lisa PY - 2018/12/18 TI - The Use and Effects of Electronic Health Tools for Patient Self-Monitoring and Reporting of Outcomes Following Medication Use: Systematic Review JO - J Med Internet Res SP - e294 VL - 20 IS - 12 KW - eHealth KW - mHealth KW - electronic health record KW - telemedicine KW - self-report KW - patient portals KW - patient-centered care KW - drug monitoring KW - adverse effects N2 - Background: Electronic health (eHealth) tools are becoming increasingly popular for helping patients? self-manage chronic conditions. Little research, however, has examined the effect of patients using eHealth tools to self-report their medication management and use. Similarly, there is little evidence showing how eHealth tools might prompt patients and health care providers to make appropriate changes to medication use. Objective: The objective of this systematic review was to determine the impact of patients? use of eHealth tools on self-reporting adverse effects and symptoms that promote changes to medication use. Related secondary outcomes were also evaluated. Methods: MEDLINE, EMBASE, and CINAHL were searched from January 1, 2000, to April 25, 2018. Reference lists of relevant systematic reviews and included articles from the literature search were also screened to identify relevant studies. Title, abstract, and full-text review as well as data extraction and risk of bias assessment were performed independently by 2 reviewers. Due to high heterogeneity, results were not meta-analyzed and instead presented as a narrative synthesis. Results: A total of 14 studies, including 13 randomized controlled trials (RCTs) and 1 open-label intervention, were included, from which 11 unique eHealth tools were identified. In addition, 14 RCTs found statistically significant increases in positive medication changes as a result of using eHealth tools, as did the single open-label study. Moreover, 8 RCTs found improvement in patient symptoms following eHealth tool use, especially in adolescent asthma patients. Furthermore, 3 RCTs showed that eHealth tools might improve patient self-efficacy and self-management of chronic disease. Little or no evidence was found to support the effectiveness of eHealth tools at improving medication recommendations and reconciliation by clinicians, medication-use behavior, health service utilization, adverse effects, quality of life, or patient satisfaction. eHealth tools with multifaceted functionalities and those allowing direct patient-provider communication may be more effective at improving patient self-management and self-efficacy. Conclusions: Evidence suggests that the use of eHealth tools may improve patient symptoms and lead to medication changes. Patients generally found eHealth tools useful in improving communication with health care providers. Moreover, health-related outcomes among frequent eHealth tool users improved in comparison with individuals who did not use eHealth tools frequently. Implementation issues such as poor patient engagement and poor clinician workflow integration were identified. More high-quality research is needed to explore how eHealth tools can be used to effectively manage use of medications to improve medication management and patient outcomes. UR - https://www.jmir.org/2018/12/e294/ UR - http://dx.doi.org/10.2196/jmir.9284 UR - http://www.ncbi.nlm.nih.gov/pubmed/30563822 ID - info:doi/10.2196/jmir.9284 ER - TY - JOUR AU - Li, Fei AU - Liu, Weisong AU - Yu, Hong PY - 2018/11/26 TI - Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning JO - JMIR Med Inform SP - e12159 VL - 6 IS - 4 KW - adverse drug event KW - deep learning KW - multi-task learning KW - named entity recognition KW - natural language processing KW - relation extraction N2 - Background: Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs. Objective: We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps?named entity recognition and relation extraction?our second objective was to improve the deep learning model using multi-task learning between the two steps. Methods: We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively. Results: Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance. Conclusions: Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning. UR - http://medinform.jmir.org/2018/4/e12159/ UR - http://dx.doi.org/10.2196/12159 UR - http://www.ncbi.nlm.nih.gov/pubmed/30478023 ID - info:doi/10.2196/12159 ER - TY - JOUR AU - Kürzinger, Marie-Laure AU - Schück, Stéphane AU - Texier, Nathalie AU - Abdellaoui, Redhouane AU - Faviez, Carole AU - Pouget, Julie AU - Zhang, Ling AU - Tcherny-Lessenot, Stéphanie AU - Lin, Stephen AU - Juhaeri, Juhaeri PY - 2018/11/20 TI - Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis JO - J Med Internet Res SP - e10466 VL - 20 IS - 11 KW - adverse event KW - internet KW - medical forums KW - pharmacovigilance KW - signal detection KW - signals of disproportionate reporting KW - social media N2 - Background: While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs). Objective: This study aimed (1) to assess the consistency of SDRs detected from patients? medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems. Methods: Messages posted on patients? forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided. Results: The comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase. Conclusions: The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients? medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals. UR - http://www.jmir.org/2018/11/e10466/ UR - http://dx.doi.org/10.2196/10466 UR - http://www.ncbi.nlm.nih.gov/pubmed/30459145 ID - info:doi/10.2196/10466 ER - TY - JOUR AU - Park, Hyun So AU - Hong, Hee Song PY - 2018/10/24 TI - Identification of Primary Medication Concerns Regarding Thyroid Hormone Replacement Therapy From Online Patient Medication Reviews: Text Mining of Social Network Data JO - J Med Internet Res SP - e11085 VL - 20 IS - 10 KW - medication counseling KW - social network data KW - primary medication concerns KW - satisfaction with levothyroxine treatment N2 - Background: Patients with hypothyroidism report poor health-related quality of life despite having undergone thyroid hormone replacement therapy (THRT). Understanding patient concerns regarding levothyroxine can help improve the treatment outcomes of THRT. Objective: This study aimed to (1) identify the distinctive themes in patient concerns regarding THRT, (2) determine whether patients have unique primary medication concerns specific to their demographics, and (3) determine the predictability of primary medication concerns on patient treatment satisfaction. Methods: We collected patient reviews from WebMD in the United States (1037 reviews about generic levothyroxine and 1075 reviews about the brand version) posted between September 1, 2007, and January 30, 2017. We used natural language processing to identify the themes of medication concerns. Multiple regression analyses were conducted in order to examine the predictability of the primary medication concerns on patient treatment satisfaction. Results: Natural language processing of the patient reviews of levothyroxine posted on a social networking site produced 6 distinctive themes of patient medication concerns related to levothyroxine treatment: how to take the drug, treatment initiation, dose adjustment, symptoms of pain, generic substitutability, and appearance. Patients had different primary medication concerns unique to their gender, age, and treatment duration. Furthermore, treatment satisfaction on levothyroxine depended on what primary medication concerns the patient had. Conclusions: Natural language processing of text content available on social media could identify different themes of patient medication concerns that can be validated in future studies to inform the design of tailored medication counseling for improved patient treatment satisfaction. UR - http://www.jmir.org/2018/10/e11085/ UR - http://dx.doi.org/10.2196/11085 UR - http://www.ncbi.nlm.nih.gov/pubmed/30355555 ID - info:doi/10.2196/11085 ER - TY - JOUR AU - Usui, Misa AU - Aramaki, Eiji AU - Iwao, Tomohide AU - Wakamiya, Shoko AU - Sakamoto, Tohru AU - Mochizuki, Mayumi PY - 2018/09/27 TI - Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in Japanese JO - JMIR Med Inform SP - e11021 VL - 6 IS - 3 KW - adverse drug events KW - natural language processing KW - medical informatics KW - medication history KW - pharmacovigilance N2 - Background: Despite the growing number of studies using natural language processing for pharmacovigilance, there are few reports on manipulating free text patient information in Japanese. Objective: This study aimed to establish a method of extracting and standardizing patient complaints from electronic medication histories accumulated in a Japanese community pharmacy for the detection of possible adverse drug event (ADE) signals. Methods: Subjective information included in electronic medication history data provided by a Japanese pharmacy operating in Hiroshima, Japan from September 1, 2015 to August 31, 2016, was used as patients? complaints. We formulated search rules based on morphological analysis and daily (nonmedical) speech and developed a system that automatically executes the search rules and annotates free text data with International Classification of Diseases, Tenth Revision (ICD-10) codes. The performance of the system was evaluated through comparisons with data manually annotated by health care workers for a data set of 5000 complaints. Results: Of 5000 complaints, the system annotated 2236 complaints with ICD-10 codes, whereas health care workers annotated 2348 statements. There was a match in the annotation of 1480 complaints between the system and manual work. System performance was .66 regarding precision, .63 in recall, and .65 for the F-measure. Conclusions: Our results suggest that the system may be helpful in extracting and standardizing patients? speech related to symptoms from massive amounts of free text data, replacing manual work. After improving the extraction accuracy, we expect to utilize this system to detect signals of possible ADEs from patients? complaints in the future. UR - http://medinform.jmir.org/2018/3/e11021/ UR - http://dx.doi.org/10.2196/11021 UR - http://www.ncbi.nlm.nih.gov/pubmed/30262450 ID - info:doi/10.2196/11021 ER - TY - JOUR AU - Brajovic, Sonja AU - Blaser, A. David AU - Zisk, Meaghan AU - Caligtan, Christine AU - Okun, Sally AU - Hall, Marni AU - Pamer, A. Carol PY - 2018/08/21 TI - Validating a Framework for Coding Patient-Reported Health Information to the Medical Dictionary for Regulatory Activities Terminology: An Evaluative Study JO - JMIR Med Inform SP - e42 VL - 6 IS - 3 KW - adverse drug events KW - Food and Drug Administration KW - MedDRA KW - patient-generated health data KW - PatientsLikeMe KW - vocabulary, controlled KW - data curation N2 - Background: The availability of and interest in patient-generated health data (PGHD) have grown steadily. Patients describe medical experiences differently compared with how clinicians or researchers would describe their observations of those same experiences. Patients may find nonserious, known adverse drug events (ADEs) to be an ongoing concern, which impacts the tolerability and adherence. Clinicians must be vigilant for medically serious, potentially fatal ADEs. Having both perspectives provides patients and clinicians with a complete picture of what to expect from drug therapies. Multiple initiatives seek to incorporate patients? perspectives into drug development, including PGHD exploration for pharmacovigilance. The Food and Drug Administration (FDA) Adverse Event Reporting System contains case reports of postmarketing ADEs. To facilitate the analysis of these case reports, case details are coded using the Medical Dictionary for Regulatory Activities (MedDRA). PatientsLikeMe is a Web-based network where patients report, track, share, and discuss their health information. PatientsLikeMe captures PGHD through free-text and structured data fields. PatientsLikeMe structured data are coded to multiple medical terminologies, including MedDRA. The standardization of PatientsLikeMe PGHD enables electronic accessibility and enhances patient engagement. Objective: The aim of this study is to retrospectively review PGHD for symptoms and ADEs entered by patients on PatientsLikeMe and coded by PatientsLikeMe to MedDRA terminology for concordance with regulatory-focused coding practices. Methods: An FDA MedDRA coding expert retrospectively reviewed a data file containing verbatim patient-reported symptoms and ADEs and PatientsLikeMe-assigned MedDRA terms to determine the medical accuracy and appropriateness of the selected MedDRA terms, applying the International Council for Harmonisation MedDRA Term Selection: Points to Consider (MTS:PTC) guides. Results: The FDA MedDRA coding expert reviewed 3234 PatientsLikeMe-assigned MedDRA codes and patient-reported verbatim text. The FDA and PatientsLikeMe were concordant at 97.09% (3140/3234) of the PatientsLikeMe-assigned MedDRA codes. The 2.91% (94/3234) discordant subset was analyzed to identify reasons for differences. Coding differences were attributed to several reasons but mostly driven by PatientsLikeMe?s approach of assigning a more general MedDRA term to enable patient-to-patient engagement, while the FDA assigned a more specific medically relevant term. Conclusions: PatientsLikeMe MedDRA coding of PGHD was generally comparable to how the FDA would code similar data, applying the MTS:PTC principles. Discordant coding resulted from several reasons but mostly reflected a difference in purpose. The MTS:PTC coding principles aim to capture the most specific reported information about an ADE, whereas PatientsLikeMe may code patient-reported symptoms and ADEs to more general MedDRA terms to support patient engagement among a larger group of patients. This study demonstrates that most verbatim reports of symptoms and ADEs collected by a PGHD source, such as the PatientsLikeMe platform, could be reliably coded to MedDRA terminology by applying the MTS:PTC guide. Regarding all secondary use of novel data, understanding coding and standardization principles applied to these data types are important. UR - http://medinform.jmir.org/2018/3/e42/ UR - http://dx.doi.org/10.2196/medinform.9878 UR - http://www.ncbi.nlm.nih.gov/pubmed/30131314 ID - info:doi/10.2196/medinform.9878 ER - TY - JOUR AU - Wang, Mei-Hua AU - Chen, Han-Kun AU - Hsu, Min-Huei AU - Wang, Hui-Chi AU - Yeh, Yu-Ting PY - 2018/08/08 TI - Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System JO - J Med Internet Res SP - e10886 VL - 20 IS - 8 KW - laboratory autoreporting system KW - HALR KW - electronic medical records N2 - Background: Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases. Objective: The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. Methods: We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity. Results: The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients? diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively. Conclusions: The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases? clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases. UR - http://www.jmir.org/2018/8/e10886/ UR - http://dx.doi.org/10.2196/10886 UR - http://www.ncbi.nlm.nih.gov/pubmed/30089608 ID - info:doi/10.2196/10886 ER - TY - JOUR AU - Schoen, W. Martin AU - Basch, Ethan AU - Hudson, L. Lori AU - Chung, E. Arlene AU - Mendoza, R. Tito AU - Mitchell, A. Sandra AU - St. Germain, Diane AU - Baumgartner, Paul AU - Sit, Laura AU - Rogak, J. Lauren AU - Shouery, Marwan AU - Shalley, Eve AU - Reeve, B. Bryce AU - Fawzy, R. Maria AU - Bhavsar, A. Nrupen AU - Cleeland, Charles AU - Schrag, Deborah AU - Dueck, C. Amylou AU - Abernethy, P. Amy PY - 2018/07/16 TI - Software for Administering the National Cancer Institute?s Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events: Usability Study JO - JMIR Hum Factors SP - e10070 VL - 5 IS - 3 KW - usability KW - patient-reported outcomes KW - symptoms KW - adverse events KW - PRO-CTCAE KW - cancer clinical trials N2 - Background: The US National Cancer Institute (NCI) developed software to gather symptomatic adverse events directly from patients participating in clinical trials. The software administers surveys to patients using items from the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) through Web-based or automated telephone interfaces and facilitates the management of survey administration and the resultant data by professionals (clinicians and research associates). Objective: The purpose of this study was to iteratively evaluate and improve the usability of the PRO-CTCAE software. Methods: Heuristic evaluation of the software functionality was followed by semiscripted, think-aloud protocols in two consecutive rounds of usability testing among patients with cancer, clinicians, and research associates at 3 cancer centers. We conducted testing with patients both in clinics and at home (remotely) for both Web-based and telephone interfaces. Furthermore, we refined the software between rounds and retested. Results: Heuristic evaluation identified deviations from the best practices across 10 standardized categories, which informed initial software improvement. Subsequently, we conducted user-based testing among 169 patients and 47 professionals. Software modifications between rounds addressed identified issues, including difficulty using radio buttons, absence of survey progress indicators, and login problems (for patients) as well as scheduling of patient surveys (for professionals). The initial System Usability Scale (SUS) score for the patient Web-based interface was 86 and 82 (P=.22) before and after modifications, respectively, whereas the task completion score was 4.47, which improved to 4.58 (P=.39) after modifications. Following modifications for professional users, the SUS scores improved from 71 to 75 (P=.47), and the mean task performance improved significantly (4.40 vs 4.02; P=.001). Conclusions: Software modifications, informed by rigorous assessment, rendered a usable system, which is currently used in multiple NCI-sponsored multicenter cancer clinical trials. Trial Registration: ClinicalTrials.gov NCT01031641; https://clinicaltrials.gov/ct2/show/NCT01031641 (Archived by WebCite at http://www.webcitation.org/708hTjlTl) UR - http://humanfactors.jmir.org/2018/3/e10070/ UR - http://dx.doi.org/10.2196/10070 UR - http://www.ncbi.nlm.nih.gov/pubmed/30012546 ID - info:doi/10.2196/10070 ER - TY - JOUR AU - Peddie, David AU - Small, S. Serena AU - Badke, Katherin AU - Bailey, Chantelle AU - Balka, Ellen AU - Hohl, M. Corinne PY - 2018/06/28 TI - Adverse Drug Event Reporting From Clinical Care: Mixed-Methods Analysis for a Minimum Required Dataset JO - JMIR Med Inform SP - e10248 VL - 6 IS - 2 KW - adverse drug event KW - adverse drug reaction KW - data fields KW - dataset KW - reporting KW - pharmacovigilance KW - mixed-methods KW - clinician-informed design N2 - Background: Patients commonly transition between health care settings, requiring care providers to transfer medication utilization information. Yet, information sharing about adverse drug events (ADEs) remains nonstandardized. Objective: The objective of our study was to describe a minimum required dataset for clinicians to document and communicate ADEs to support clinical decision making and improve patient safety. Methods: We used mixed-methods analysis to design a minimum required dataset for ADE documentation and communication. First, we completed a systematic review of the existing ADE reporting systems. After synthesizing reporting concepts and data fields, we conducted fieldwork to inform the design of a preliminary reporting form. We presented this information to clinician end-user groups to establish a recommended dataset. Finally, we pilot-tested and refined the dataset in a paper-based format. Results: We evaluated a total of 1782 unique data fields identified in our systematic review that describe the reporter, patient, ADE, and suspect and concomitant drugs. Of these, clinicians requested that 26 data fields be integrated into the dataset. Avoiding the need to report information already available electronically, reliance on prospective rather than retrospective causality assessments, and omitting fields deemed irrelevant to clinical care were key considerations. Conclusions: By attending to the information needs of clinicians, we developed a standardized dataset for adverse drug event reporting. This dataset can be used to support communication between care providers and integrated into electronic systems to improve patient safety. If anonymized, these standardized data may be used for enhanced pharmacovigilance and research activities. UR - http://medinform.jmir.org/2018/2/e10248/ UR - http://dx.doi.org/10.2196/10248 UR - http://www.ncbi.nlm.nih.gov/pubmed/29954724 ID - info:doi/10.2196/10248 ER - TY - JOUR AU - Keller, Sophie Michelle AU - Mosadeghi, Sasan AU - Cohen, R. Erica AU - Kwan, James AU - Spiegel, Ross Brennan Mason PY - 2018/06/11 TI - Reproductive Health and Medication Concerns for Patients With Inflammatory Bowel Disease: Thematic and Quantitative Analysis Using Social Listening JO - J Med Internet Res SP - e206 VL - 20 IS - 6 KW - pregnancy KW - breastfeeding KW - reproductive health KW - social media KW - medication adherence KW - infodemiology KW - pharmacovigilance N2 - Background: Inflammatory bowel disease (IBD) affects many individuals of reproductive age. Most IBD medications are safe to use during pregnancy and breastfeeding; however, observational studies find that women with IBD have higher rates of voluntary childlessness due to fears about medication use during pregnancy. Understanding why and how individuals with IBD make decisions about medication adherence during important reproductive periods can help clinicians address patient fears about medication use. Objective: The objective of this study was to gain a more thorough understanding of how individuals taking IBD medications during key reproductive periods make decisions about their medication use. Methods: We collected posts from 3000 social media sites posted over a 3-year period and analyzed the posts using qualitative descriptive content analysis. The first level of analysis, open coding, identified individual concepts present in the social media posts. We subsequently created a codebook from significant or frequently occurring codes in the data. After creating the codebook, we reviewed the data and coded using our focused codes. We organized the focused codes into larger thematic categories. Results: We identified 7 main themes in 1818 social media posts. Individuals used social media to (1) seek advice about medication use related to reproductive health (13.92%, 252/1818); (2) express beliefs about the safety of IBD therapies (7.43%, 135/1818); (3) discuss personal experiences with medication use (16.72%, 304/1818); (4) articulate fears and anxieties about the safety of IBD therapies (11.55%, 210/1818); (5) discuss physician-patient relationships (3.14%, 57/1818); (6) address concerns around conception, infertility, and IBD medications (17.38%, 316/1818); and (7) talk about IBD symptoms during and after pregnancy and breastfeeding periods (11.33%, 206/1818). Conclusions: Beliefs around medication safety play an important role in whether individuals with IBD decide to take medications during pregnancy and breastfeeding. Having a better understanding about why patients stop or refuse to take certain medications during key reproductive periods may allow clinicians to address specific beliefs and attitudes during office visits. UR - http://www.jmir.org/2018/6/e206/ UR - http://dx.doi.org/10.2196/jmir.9870 UR - http://www.ncbi.nlm.nih.gov/pubmed/29891471 ID - info:doi/10.2196/jmir.9870 ER - TY - JOUR AU - Musy, N. Sarah AU - Ausserhofer, Dietmar AU - Schwendimann, René AU - Rothen, Ulrich Hans AU - Jeitziner, Marie-Madlen AU - Rutjes, WS Anne AU - Simon, Michael PY - 2018/05/30 TI - Trigger Tool?Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review JO - J Med Internet Res SP - e198 VL - 20 IS - 5 KW - patient safety KW - electronic health records KW - patient harm KW - review, systematic N2 - Background: Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. Objective: The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies? designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard. Methods: PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. Results: A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0% to 17.9%, with a median of 0.8%. The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8% to 60%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20% to 91%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9% to 83.3%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0% to 60%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4%, 10.5%, 71.1%, and 68.4% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. Conclusions: We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies. UR - http://www.jmir.org/2018/5/e198/ UR - http://dx.doi.org/10.2196/jmir.9901 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/jmir.9901 ER - TY - JOUR AU - Dijkstra, Elske Nienke AU - Sino, Maria Carolina Geertruida AU - Heerdink, Rob Eibert AU - Schuurmans, Joanna Marieke PY - 2018/03/07 TI - Development of eHOME, a Mobile Instrument for Reporting, Monitoring, and Consulting Drug-Related Problems in Home Care: Human-Centered Design Study JO - JMIR Hum Factors SP - e10 VL - 5 IS - 1 KW - primary care KW - home care KW - eHealth KW - mHealth N2 - Background: Home care patients often use many medications and are prone to drug-related problems (DRPs). For the management of problems related to drug use, home care could add to the multidisciplinary expertise of general practitioners (GPs) and pharmacists. The home care observation of medication-related problems by home care employees (HOME)-instrument is paper-based and assists home care workers in reporting potential DRPs. To facilitate the multiprofessional consultation, a digital report of DRPs from the HOME-instrument and digital monitoring and consulting of DRPs between home care and general practices and pharmacies is desired. Objective: The objective of this study was to develop an electronic HOME system (eHOME), a mobile version of the HOME-instrument that includes a monitoring and a consulting system for primary care. Methods: The development phase of the Medical Research Council (MRC) framework was followed in which an iterative human-centered design (HCD) approach was applied. The approach involved a Delphi round for the context of use and user requirements analysis of the digital HOME-instrument and the monitoring and consulting system followed by 2 series of pilots for testing the usability and redesign. Results: By using an iterative design approach and by involving home care workers, GPs, and pharmacists throughout the process as informants, design partners, and testers, important aspects that were crucial for system realization and user acceptance were revealed. Through the report webpage interface, which includes the adjusted content of the HOME-instrument and added home care practice?based problems, home care workers can digitally report observed DRPs. Furthermore, it was found that the monitoring and consulting webpage interfaces enable digital consultation between home care and general practices and pharmacies. The webpages were considered convenient, clear, easy, and usable. Conclusions: By employing an HCD approach, the eHOME-instrument was found to be an easy-to-use system. The systematic approach promises a valuable contribution for the future development of digital mobile systems of paper-based tools. UR - http://humanfactors.jmir.org/2018/1/e10/ UR - http://dx.doi.org/10.2196/humanfactors.8319 UR - http://www.ncbi.nlm.nih.gov/pubmed/29514771 ID - info:doi/10.2196/humanfactors.8319 ER - TY - JOUR AU - Fleming, N. James AU - Treiber, Frank AU - McGillicuddy, John AU - Gebregziabher, Mulugeta AU - Taber, J. David PY - 2018/03/02 TI - Improving Transplant Medication Safety Through a Pharmacist-Empowered, Patient-Centered, mHealth-Based Intervention: TRANSAFE Rx Study Protocol JO - JMIR Res Protoc SP - e59 VL - 7 IS - 3 KW - telemedicine KW - mhealth KW - transplant KW - clinical trial KW - errors KW - adherence N2 - Background: Medication errors, adverse drug events, and nonadherence are the predominant causes of graft loss in kidney transplant recipients and lead to increased healthcare utilization. Research has demonstrated that clinical pharmacists have the unique education and training to identify these events early and develop strategies to mitigate or prevent downstream sequelae. In addition, studies utilizing mHealth interventions have demonstrated success in improving the control of chronic conditions that lead to kidney transplant deterioration. Objective: The goal of the prospective, randomized TRANSAFE Rx study is to measure the clinical and economic effectiveness of a pharmacist-led, mHealth-based intervention, as compared to usual care, in kidney transplant recipients. Methods: TRANSAFE Rx is a 12-month, parallel, two-arm, 1:1 randomized controlled clinical trial involving 136 participants (68 in each arm) and measuring the clinical and economic effectiveness of a pharmacist-led intervention which utilizes an innovative mobile health application to improve medication safety and health outcomes, as compared to usual posttransplant care. Results: The primary outcome measure of this study will be the incidence and severity of MEs and ADRs, which will be identified, categorized, and compared between the intervention and control cohorts. The exploratory outcome measures of this study are to compare the incidence and severity of acute rejections, infections, graft function, graft loss, and death between research cohorts and measure the association between medication safety issues and these events. Additional data that will be gathered includes sociodemographics, health literacy, depression, and support. Conclusions: With this report we describe the study design, methods, and outcome measures that will be utilized in the ongoing TRANSAFE Rx clinical trial. Trial Registration: ClinicalTrials.gov NCT03247322: https://clinicaltrials.gov/ct2/show/NCT03247322 (Archived by WebCite at http://www.webcitation.org/6xcSUnuzW) UR - https://www.researchprotocols.org/2018/3/e59/ UR - http://dx.doi.org/10.2196/resprot.9078 UR - http://www.ncbi.nlm.nih.gov/pubmed/29500161 ID - info:doi/10.2196/resprot.9078 ER - TY - JOUR AU - Ng, Charmaine Kamela AU - Meehan, Joseph Conor AU - Torrea, Gabriela AU - Goeminne, Léonie AU - Diels, Maren AU - Rigouts, Leen AU - de Jong, Catherine Bouke AU - André, Emmanuel PY - 2018/02/27 TI - Potential Application of Digitally Linked Tuberculosis Diagnostics for Real-Time Surveillance of Drug-Resistant Tuberculosis Transmission: Validation and Analysis of Test Results JO - JMIR Med Inform SP - e12 VL - 6 IS - 1 KW - tuberculosis KW - drug resistance KW - rifampicin-resistant tuberculosis KW - rapid diagnostic tests KW - Xpert MTB/RIF KW - Genotype MTBDRplus v2.0 KW - Genoscholar NTM + MDRTB II KW - RDT probe reactions KW - rpoB mutations KW - validation and analysis KW - real-time detection N2 - Background: Tuberculosis (TB) is the highest-mortality infectious disease in the world and the main cause of death related to antimicrobial resistance, yet its surveillance is still paper-based. Rifampicin-resistant TB (RR-TB) is an urgent public health crisis. The World Health Organization has, since 2010, endorsed a series of rapid diagnostic tests (RDTs) that enable rapid detection of drug-resistant strains and produce large volumes of data. In parallel, most high-burden countries have adopted connectivity solutions that allow linking of diagnostics, real-time capture, and shared repository of these test results. However, these connected diagnostics and readily available test results are not used to their full capacity, as we have yet to capitalize on fully understanding the relationship between test results and specific rpoB mutations to elucidate its potential application to real-time surveillance. Objective: We aimed to validate and analyze RDT data in detail, and propose the potential use of connected diagnostics and associated test results for real-time evaluation of RR-TB transmission. Methods: We selected 107 RR-TB strains harboring 34 unique rpoB mutations, including 30 within the rifampicin resistance?determining region (RRDR), from the Belgian Coordinated Collections of Microorganisms, Antwerp, Belgium. We subjected these strains to Xpert MTB/RIF, GenoType MTBDRplus v2.0, and Genoscholar NTM + MDRTB II, the results of which were validated against the strains? available rpoB gene sequences. We determined the reproducibility of the results, analyzed and visualized the probe reactions, and proposed these for potential use in evaluating transmission. Results: The RDT probe reactions detected most RRDR mutations tested, although we found a few critical discrepancies between observed results and manufacturers? claims. Based on published frequencies of probe reactions and RRDR mutations, we found specific probe reactions with high potential use in transmission studies: Xpert MTB/RIF probes A, Bdelayed, C, and Edelayed; Genotype MTBDRplus v2.0 WT2, WT5, and WT6; and Genoscholar NTM + MDRTB II S1 and S3. Inspection of probe reactions of disputed mutations may potentially resolve discordance between genotypic and phenotypic test results. Conclusions: We propose a novel approach for potential real-time detection of RR-TB transmission through fully using digitally linked TB diagnostics and shared repository of test results. To our knowledge, this is the first pragmatic and scalable work in response to the consensus of world-renowned TB experts in 2016 on the potential of diagnostic connectivity to accelerate efforts to eliminate TB. This is evidenced by the ability of our proposed approach to facilitate comparison of probe reactions between different RDTs used in the same setting. Integrating this proposed approach as a plug-in module to a connectivity platform will increase usefulness of connected TB diagnostics for RR-TB outbreak detection through real-time investigation of suspected RR-TB transmission cases based on epidemiologic linking. UR - http://medinform.jmir.org/2018/1/e12/ UR - http://dx.doi.org/10.2196/medinform.9309 UR - http://www.ncbi.nlm.nih.gov/pubmed/29487047 ID - info:doi/10.2196/medinform.9309 ER - TY - JOUR AU - Hohl, M. Corinne AU - Small, S. Serena AU - Peddie, David AU - Badke, Katherin AU - Bailey, Chantelle AU - Balka, Ellen PY - 2018/02/27 TI - Why Clinicians Don?t Report Adverse Drug Events: Qualitative Study JO - JMIR Public Health Surveill SP - e21 VL - 4 IS - 1 KW - adverse events KW - pharmacovigilance KW - drug safety KW - adverse drug reaction KW - adverse drug event KW - electronic health records KW - information and technology KW - medication reconciliation KW - qualitative research N2 - Background: Adverse drug events are unintended and harmful events related to medications. Adverse drug events are important for patient care, quality improvement, drug safety research, and postmarketing surveillance, but they are vastly underreported. Objective: Our objectives were to identify barriers to adverse drug event documentation and factors contributing to underreporting. Methods: This qualitative study was conducted in 1 ambulatory center, and the emergency departments and inpatient wards of 3 acute care hospitals in British Columbia between March 2014 and December 2016. We completed workplace observations and focus groups with general practitioners, hospitalists, emergency physicians, and hospital and community pharmacists. We analyzed field notes by coding and iteratively analyzing our data to identify emerging concepts, generate thematic and event summaries, and create workflow diagrams. Clinicians validated emerging concepts by applying them to cases from their clinical practice. Results: We completed 238 hours of observations during which clinicians investigated 65 suspect adverse drug events. The observed events were often complex and diagnosed over time, requiring the input of multiple providers. Providers documented adverse drug events in charts to support continuity of care but never reported them to external agencies. Providers faced time constraints, and reporting would have required duplication of documentation. Conclusions: Existing reporting systems are not suited to capture the complex nature of adverse drug events or adapted to workflow and are simply not used by frontline clinicians. Systems that are integrated into electronic medical records, make use of existing data to avoid duplication of documentation, and generate alerts to improve safety may address the shortcomings of existing systems and generate robust adverse drug event data as a by-product of safer care. UR - http://publichealth.jmir.org/2018/1/e21/ UR - http://dx.doi.org/10.2196/publichealth.9282 UR - http://www.ncbi.nlm.nih.gov/pubmed/29487041 ID - info:doi/10.2196/publichealth.9282 ER - TY - JOUR AU - Yang, Cheng-Yi AU - Lo, Yu-Sheng AU - Chen, Ray-Jade AU - Liu, Chien-Tsai PY - 2018/1/19 TI - A Clinical Decision Support Engine Based on a National Medication Repository for the Detection of Potential Duplicate Medications: Design and Evaluation JO - JMIR Med Inform SP - e6 VL - 6 IS - 1 KW - duplicate medication KW - adverse drug reaction KW - clinical decision support system KW - PharmaCloud N2 - Background: A computerized physician order entry (CPOE) system combined with a clinical decision support system can reduce duplication of medications and thus adverse drug reactions. However, without infrastructure that supports patients? integrated medication history across health care facilities nationwide, duplication of medication can still occur. In Taiwan, the National Health Insurance Administration has implemented a national medication repository and Web-based query system known as the PharmaCloud, which allows physicians to access their patients? medication records prescribed by different health care facilities across Taiwan. Objective: This study aimed to develop a scalable, flexible, and thematic design-based clinical decision support (CDS) engine, which integrates a national medication repository to support CPOE systems in the detection of potential duplication of medication across health care facilities, as well as to analyze its impact on clinical encounters. Methods: A CDS engine was developed that can download patients? up-to-date medication history from the PharmaCloud and support a CPOE system in the detection of potential duplicate medications. When prescribing a medication order using the CPOE system, a physician receives an alert if there is a potential duplicate medication. To investigate the impact of the CDS engine on clinical encounters in outpatient services, a clinical encounter log was created to collect information about time, prescribed drugs, and physicians? responses to handling the alerts for each encounter. Results: The CDS engine was installed in a teaching affiliate hospital, and the clinical encounter log collected information for 3 months, during which a total of 178,300 prescriptions were prescribed in the outpatient departments. In all, 43,844/178,300 (24.59%) patients signed the PharmaCloud consent form allowing their physicians to access their medication history in the PharmaCloud. The rate of duplicate medication was 5.83% (1843/31,614) of prescriptions. When prescribing using the CDS engine, the median encounter time was 4.3 (IQR 2.3-7.3) min, longer than that without using the CDS engine (median 3.6, IQR 2.0-6.3 min). From the physicians? responses, we found that 42.06% (1908/4536) of the potential duplicate medications were recognized by the physicians and the medication orders were canceled. Conclusions: The CDS engine could easily extend functions for detection of adverse drug reactions when more and more electronic health record systems are adopted. Moreover, the CDS engine can retrieve more updated and completed medication histories in the PharmaCloud, so it can have better performance for detection of duplicate medications. Although our CDS engine approach could enhance medication safety, it would make for a longer encounter time. This problem can be mitigated by careful evaluation of adopted solutions for implementation of the CDS engine. The successful key component of a CDS engine is the completeness of the patient?s medication history, thus further research to assess the factors in increasing the PharmaCloud consent rate is required. UR - http://medinform.jmir.org/2018/1/e6/ UR - http://dx.doi.org/10.2196/medinform.9064 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/medinform.9064 ER - TY - JOUR AU - Sinha, S. Michael AU - Freifeld, C. Clark AU - Brownstein, S. John AU - Donneyong, M. Macarius AU - Rausch, Paula AU - Lappin, M. Brian AU - Zhou, H. Esther AU - Dal Pan, J. Gerald AU - Pawar, M. Ajinkya AU - Hwang, J. Thomas AU - Avorn, Jerry AU - Kesselheim, S. Aaron PY - 2018/01/05 TI - Social Media Impact of the Food and Drug Administration's Drug Safety Communication Messaging About Zolpidem: Mixed-Methods Analysis JO - JMIR Public Health Surveill SP - e1 VL - 4 IS - 1 KW - Food and Drug Administration KW - drug safety communications KW - surveillance KW - epidemiology KW - social media KW - Twitter KW - Facebook KW - Google Trends N2 - Background: The Food and Drug Administration (FDA) issues drug safety communications (DSCs) to health care professionals, patients, and the public when safety issues emerge related to FDA-approved drug products. These safety messages are disseminated through social media to ensure broad uptake. Objective: The objective of this study was to assess the social media dissemination of 2 DSCs released in 2013 for the sleep aid zolpidem. Methods: We used the MedWatcher Social program and the DataSift historic query tool to aggregate Twitter and Facebook posts from October 1, 2012 through August 31, 2013, a period beginning approximately 3 months before the first DSC and ending 3 months after the second. Posts were categorized as (1) junk, (2) mention, and (3) adverse event (AE) based on a score between ?0.2 (completely unrelated) to 1 (perfectly related). We also looked at Google Trends data and Wikipedia edits for the same time period. Google Trends search volume is scaled on a range of 0 to 100 and includes ?Related queries? during the relevant time periods. An interrupted time series (ITS) analysis assessed the impact of DSCs on the counts of posts with specific mention of zolpidem-containing products. Chow tests for known structural breaks were conducted on data from Twitter, Facebook, and Google Trends. Finally, Wikipedia edits were pulled from the website?s editorial history, which lists all revisions to a given page and the editor?s identity. Results: In total, 174,286 Twitter posts and 59,641 Facebook posts met entry criteria. Of those, 16.63% (28,989/174,286) of Twitter posts and 25.91% (15,453/59,641) of Facebook posts were labeled as junk and excluded. AEs and mentions represented 9.21% (16,051/174,286) and 74.16% (129,246/174,286) of Twitter posts and 5.11% (3,050/59,641) and 68.98% (41,138/59,641) of Facebook posts, respectively. Total daily counts of posts about zolpidem-containing products increased on Twitter and Facebook on the day of the first DSC; Google searches increased on the week of the first DSC. ITS analyses demonstrated variability but pointed to an increase in interest around the first DSC. Chow tests were significant (P<.0001) for both DSCs on Facebook and Twitter, but only the first DSC on Google Trends. Wikipedia edits occurred soon after each DSC release, citing news articles rather than the DSC itself and presenting content that needed subsequent revisions for accuracy. Conclusions: Social media offers challenges and opportunities for dissemination of the DSC messages. The FDA could consider strategies for more actively disseminating DSC safety information through social media platforms, particularly when announcements require updating. The FDA may also benefit from directly contributing content to websites like Wikipedia that are frequently accessed for drug-related information. UR - http://publichealth.jmir.org/2018/1/e1/ UR - http://dx.doi.org/10.2196/publichealth.7823 UR - http://www.ncbi.nlm.nih.gov/pubmed/29305342 ID - info:doi/10.2196/publichealth.7823 ER - TY - JOUR AU - P Tafti, Ahmad AU - Badger, Jonathan AU - LaRose, Eric AU - Shirzadi, Ehsan AU - Mahnke, Andrea AU - Mayer, John AU - Ye, Zhan AU - Page, David AU - Peissig, Peggy PY - 2017/12/08 TI - Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure JO - JMIR Med Inform SP - e51 VL - 5 IS - 4 KW - adverse drug event KW - adverse drug reaction KW - drug side effects KW - machine learning KW - text mining N2 - Background: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. Objective: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. Methods: We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. Results: The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. Conclusions: To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis. UR - http://medinform.jmir.org/2017/4/e51/ UR - http://dx.doi.org/10.2196/medinform.9170 UR - http://www.ncbi.nlm.nih.gov/pubmed/29222076 ID - info:doi/10.2196/medinform.9170 ER - TY - JOUR AU - Kim, Jung Sunny AU - Marsch, A. Lisa AU - Hancock, T. Jeffrey AU - Das, K. Amarendra PY - 2017/10/31 TI - Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data JO - J Med Internet Res SP - e353 VL - 19 IS - 10 KW - opioid epidemic KW - opioid crisis KW - opioid-related disorders KW - substance use KW - substance-related disorders KW - prescription drug misuse KW - addiction KW - Facebook KW - Twitter KW - Instagram KW - big data KW - ethics N2 - Background: Substance use?related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the promising potential to utilize social media big data to monitor and treat substance use problems, the characteristics, mechanisms, and outcomes of substance use?related communications on social media are largely unknown. Understanding these aspects can help researchers effectively leverage social media big data and platforms for observation and health communication outreach for people with substance use problems. Objective: The objective of this critical review was to determine how social media big data can be used to understand communication and behavioral patterns of problematic use of prescription drugs. We elaborate on theoretical applications, ethical challenges and methodological considerations when using social media big data for research on drug abuse and addiction. Based on a critical review process, we propose a typology with key initiatives to address the knowledge gap in the use of social media for research on prescription drug abuse and addiction. Methods: First, we provided a narrative summary of the literature on drug use?related communication on social media. We also examined ethical considerations in the research processes of (1) social media big data mining, (2) subgroup or follow-up investigation, and (3) dissemination of social media data-driven findings. To develop a critical review-based typology, we searched the PubMed database and the entire e-collection theme of ?infodemiology and infoveillance? in the Journal of Medical Internet Research / JMIR Publications. Studies that met our inclusion criteria (eg, use of social media data concerning non-medical use of prescription drugs, data informatics-driven findings) were reviewed for knowledge synthesis. User characteristics, communication characteristics, mechanisms and predictors of such communications, and the psychological and behavioral outcomes of social media use for problematic drug use?related communications are the dimensions of our typology. In addition to ethical practices and considerations, we also reviewed the methodological and computational approaches used in each study to develop our typology. Results: We developed a typology to better understand non-medical, problematic use of prescription drugs through the lens of social media big data. Highly relevant studies that met our inclusion criteria were reviewed for knowledge synthesis. The characteristics of users who shared problematic substance use?related communications on social media were reported by general group terms, such as adolescents, Twitter users, and Instagram users. All reviewed studies examined the communication characteristics, such as linguistic properties, and social networks of problematic drug use?related communications on social media. The mechanisms and predictors of such social media communications were not directly examined or empirically identified in the reviewed studies. The psychological or behavioral consequence (eg, increased behavioral intention for mimicking risky health behaviors) of engaging with and being exposed to social media communications regarding problematic drug use was another area of research that has been understudied. Conclusions: We offer theoretical applications, ethical considerations, and empirical evidence within the scope of social media communication and prescription drug abuse and addiction. Our critical review suggests that social media big data can be a tremendous resource to understand, monitor and intervene on drug abuse and addiction problems. UR - http://www.jmir.org/2017/10/e353/ UR - http://dx.doi.org/10.2196/jmir.6426 UR - http://www.ncbi.nlm.nih.gov/pubmed/29089287 ID - info:doi/10.2196/jmir.6426 ER - TY - JOUR AU - Bousquet, Cedric AU - Dahamna, Badisse AU - Guillemin-Lanne, Sylvie AU - Darmoni, J. Stefan AU - Faviez, Carole AU - Huot, Charles AU - Katsahian, Sandrine AU - Leroux, Vincent AU - Pereira, Suzanne AU - Richard, Christophe AU - Schück, Stéphane AU - Souvignet, Julien AU - Lillo-Le Louët, Agnès AU - Texier, Nathalie PY - 2017/09/21 TI - The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process JO - JMIR Res Protoc SP - e179 VL - 6 IS - 9 KW - pharmacovigilance KW - social media KW - big data KW - natural language processing KW - medical terminology N2 - Background: Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. Objective: This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. Methods: In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Results: Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. Conclusions: We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. UR - http://www.researchprotocols.org/2017/9/e179/ UR - http://dx.doi.org/10.2196/resprot.6463 UR - http://www.ncbi.nlm.nih.gov/pubmed/28935617 ID - info:doi/10.2196/resprot.6463 ER - TY - JOUR AU - Kagashe, Ireneus AU - Yan, Zhijun AU - Suheryani, Imran PY - 2017/09/12 TI - Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data JO - J Med Internet Res SP - e315 VL - 19 IS - 9 KW - machine learning KW - Twitter messaging KW - social media KW - disease outbreaks KW - influenza KW - public health surveillance KW - natural language processing KW - influenza vaccines N2 - Background: Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques. Objective: Using Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance. Methods: From tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs? tweets using latent Dirichlet allocation (LDA). Results: Our proposed classifier obtained an F1 score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks. Conclusions: The study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases. UR - http://www.jmir.org/2017/9/e315/ UR - http://dx.doi.org/10.2196/jmir.7393 UR - http://www.ncbi.nlm.nih.gov/pubmed/28899847 ID - info:doi/10.2196/jmir.7393 ER - TY - JOUR AU - Abdellaoui, Redhouane AU - Schück, Stéphane AU - Texier, Nathalie AU - Burgun, Anita PY - 2017/06/22 TI - Filtering Entities to Optimize Identification of Adverse Drug Reaction From Social Media: How Can the Number of Words Between Entities in the Messages Help? JO - JMIR Public Health Surveill SP - e36 VL - 3 IS - 2 KW - pharmacovigilance KW - social media KW - text mining KW - Gaussian mixture model KW - EM algorithm KW - clustering KW - density estimation N2 - Background: With the increasing popularity of Web 2.0 applications, social media has made it possible for individuals to post messages on adverse drug reactions. In such online conversations, patients discuss their symptoms, medical history, and diseases. These disorders may correspond to adverse drug reactions (ADRs) or any other medical condition. Therefore, methods must be developed to distinguish between false positives and true ADR declarations. Objective: The aim of this study was to investigate a method for filtering out disorder terms that did not correspond to adverse events by using the distance (as number of words) between the drug term and the disorder or symptom term in the post. We hypothesized that the shorter the distance between the disorder name and the drug, the higher the probability to be an ADR. Methods: We analyzed a corpus of 648 messages corresponding to a total of 1654 (drug and disorder) pairs from 5 French forums using Gaussian mixture models and an expectation-maximization (EM) algorithm . Results: The distribution of the distances between the drug term and the disorder term enabled the filtering of 50.03% (733/1465) of the disorders that were not ADRs. Our filtering strategy achieved a precision of 95.8% and a recall of 50.0%. Conclusions: This study suggests that such distance between terms can be used for identifying false positives, thereby improving ADR detection in social media. UR - http://publichealth.jmir.org/2017/2/e36/ UR - http://dx.doi.org/10.2196/publichealth.6577 UR - http://www.ncbi.nlm.nih.gov/pubmed/28642212 ID - info:doi/10.2196/publichealth.6577 ER - TY - JOUR AU - Matsuda, Shinichi AU - Aoki, Kotonari AU - Tomizawa, Shiho AU - Sone, Masayoshi AU - Tanaka, Riwa AU - Kuriki, Hiroshi AU - Takahashi, Yoichiro PY - 2017/02/24 TI - Analysis of Patient Narratives in Disease Blogs on the Internet: An Exploratory Study of Social Pharmacovigilance JO - JMIR Public Health Surveill SP - e10 VL - 3 IS - 1 KW - Internet KW - social media KW - adverse drug reaction KW - pharmacovigilance KW - text mining N2 - Background: Although several reports have suggested that patient-generated data from Internet sources could be used to improve drug safety and pharmacovigilance, few studies have identified such data sources in Japan. We introduce a unique Japanese data source: t?by?ki, which translates literally as ?an account of a struggle with disease.? Objective: The objective of this study was to evaluate the basic characteristics of the TOBYO database, a collection of t?by?ki blogs on the Internet, and discuss potential applications for pharmacovigilance. Methods: We analyzed the overall gender and age distribution of the patient-generated TOBYO database and compared this with other external databases generated by health care professionals. For detailed analysis, we prepared separate datasets for blogs written by patients with depression and blogs written by patients with rheumatoid arthritis (RA), because these conditions were expected to entail subjective patient symptoms such as discomfort, insomnia, and pain. Frequently appearing medical terms were counted, and their variations were compared with those in an external adverse drug reaction (ADR) reporting database. Frequently appearing words regarding patients with depression and patients with RA were visualized using word clouds and word cooccurrence networks. Results: As of June 4, 2016, the TOBYO database comprised 54,010 blogs representing 1405 disorders. Overall, more entries were written by female bloggers (68.8%) than by male bloggers (30.8%). The most frequently observed disorders were breast cancer (4983 blogs), depression (3556), infertility (2430), RA (1118), and panic disorder (1090). Comparison of medical terms observed in t?by?ki blogs with those in an external ADR reporting database showed that subjective and symptomatic events and general terms tended to be frequently observed in t?by?ki blogs (eg, anxiety, headache, and pain), whereas events using more technical medical terms (eg, syndrome and abnormal laboratory test result) tended to be observed frequently in the ADR database. We also confirmed the feasibility of using visualization techniques to obtain insights from unstructured text-based t?by?ki blog data. Word clouds described the characteristics of each disorder, such as ?sleeping? and ?anxiety? in depression and ?pain? and ?painful? in RA. Conclusions: Pharmacovigilance should maintain a strong focus on patients? actual experiences, concerns, and outcomes, and this approach can be expected to uncover hidden adverse event signals earlier and to help us understand adverse events in a patient-centered way. Patient-generated t?by?ki blogs in the TOBYO database showed unique characteristics that were different from the data in existing sources generated by health care professionals. Analysis of t?by?ki blogs would add value to the assessment of disorders with a high prevalence in women, psychiatric disorders in which subjective symptoms have important clinical meaning, refractory disorders, and other chronic disorders. UR - http://publichealth.jmir.org/2017/1/e10/ UR - http://dx.doi.org/10.2196/publichealth.6872 UR - http://www.ncbi.nlm.nih.gov/pubmed/28235749 ID - info:doi/10.2196/publichealth.6872 ER - TY - JOUR AU - Perez, P. Raymond AU - Finnigan, Shanda AU - Patel, Krupa AU - Whitney, Shanell AU - Forrest, Annemarie PY - 2016/12/15 TI - Clinical Trial Electronic Portals for Expedited Safety Reporting: Recommendations from the Clinical Trials Transformation Initiative Investigational New Drug Safety Advancement Project JO - JMIR Cancer SP - e16 VL - 2 IS - 2 KW - clinical trials KW - investigational new drug application KW - risk management N2 - Background: Use of electronic clinical trial portals has increased in recent years to assist with sponsor-investigator communication, safety reporting, and clinical trial management. Electronic portals can help reduce time and costs associated with processing paperwork and add security measures; however, there is a lack of information on clinical trial investigative staff?s perceived challenges and benefits of using portals. Objective: The Clinical Trials Transformation Initiative (CTTI) sought to (1) identify challenges to investigator receipt and management of investigational new drug (IND) safety reports at oncologic investigative sites and coordinating centers and (2) facilitate adoption of best practices for communicating and managing IND safety reports using electronic portals. Methods: CTTI, a public-private partnership to improve the conduct of clinical trials, distributed surveys and conducted interviews in an opinion-gathering effort to record investigator and research staff views on electronic portals in the context of the new safety reporting requirements described in the US Food and Drug Administration?s final rule (Code of Federal Regulations Title 21 Section 312). The project focused on receipt, management, and review of safety reports as opposed to the reporting of adverse events. Results: The top challenge investigators and staff identified in using individual sponsor portals was remembering several complex individual passwords to access each site. Also, certain tasks are time-consuming (eg, downloading reports) due to slow sites or difficulties associated with particular operating systems or software. To improve user experiences, respondents suggested that portals function independently of browsers and operating systems, have intuitive interfaces with easy navigation, and incorporate additional features that would allow users to filter, search, and batch safety reports. Conclusions: Results indicate that an ideal system for sharing expedited IND safety information is through a central portal used by all sponsors. Until this is feasible, electronic reporting portals should at least have consistent functionality. CTTI has issued recommendations to improve the quality and use of electronic portals. UR - http://cancer.jmir.org/2016/2/e16/ UR - http://dx.doi.org/10.2196/cancer.6701 UR - http://www.ncbi.nlm.nih.gov/pubmed/28410179 ID - info:doi/10.2196/cancer.6701 ER - TY - JOUR AU - Tseng, Yi-Ju AU - Wu, Jung-Hsuan AU - Lin, Hui-Chi AU - Chen, Ming-Yuan AU - Ping, Xiao-Ou AU - Sun, Chun-Chuan AU - Shang, Rung-Ji AU - Sheng, Wang-Huei AU - Chen, Yee-Chun AU - Lai, Feipei AU - Chang, Shan-Chwen PY - 2015/09/21 TI - A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation JO - JMIR Med Inform SP - e31 VL - 3 IS - 3 KW - health care-associated infection KW - infection control KW - information systems KW - surveillance KW - Web-based services N2 - Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. Methods: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. Results: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. Conclusions: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system. UR - http://medinform.jmir.org/2015/3/e31/ UR - http://dx.doi.org/10.2196/medinform.4171 UR - http://www.ncbi.nlm.nih.gov/pubmed/26392229 ID - info:doi/10.2196/medinform.4171 ER - TY - JOUR PY - 2014// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e5579 VL - 6 IS - 3 UR - UR - http://dx.doi.org/10.5210/ojphi.v6i3.5579 ID - info:doi/10.5210/ojphi.v6i3.5579 ER - TY - JOUR PY - 2013// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e4432 VL - 5 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v5i1.4432 ID - info:doi/10.5210/ojphi.v5i1.4432 ER -