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Journal Description

JMIR Medical Informatics (JMI, ISSN 2291-9694) (Editor-in-chief: Christian Lovis MD MPH FACMI) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.

Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2017: 4.671), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.

JMIR Medical Informatics journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs (ready for deposit in PubMed Central/PubMed). The site is optimized for mobile and iPad use.

JMIR Medical Informatics adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics (http://www.jmir.org/issue/current).

 

Recent Articles:

  • Leuven, Belgium. Source: Flickr; Copyright: Krijn van Putten; URL: https://www.flickr.com/photos/krijnvanputten/32903556435/in/album-72157676740335264/; License: Creative Commons Attribution (CC-BY).
    Secondary Use of Clinical Data for...

    Nov 19, 2018

    Health Data for Research Through a Nationwide Privacy-Proof System in Belgium: Design and Implementation

    Authors List:

    Abstract:

    Background: Health data collected during routine care have important potential for reuse for other purposes, especially as part of a learning health system to advance the quality of care. Many sources of bias have been identified through the lifecycle of health data that could compromise the scientific integrity of these data. New data protection legislation requires research facilities to improve safety measures and, thus, ensure privacy. Objective: This study aims to address the question on how health data can be transferred from various sources and using multiple systems to a centralized platform, called Healthdata.be, while ensuring the accuracy, validity, safety, and privacy. In addition, the study demonstrates how these processes can be used in various research designs relevant for learning health systems. Methods: The Healthdata.be platform urges uniformity of the data registration at the primary source through the use of detailed clinical models. Data retrieval and transfer are organized through end-to-end encrypted electronic health channels, and data are encoded using token keys. In addition, patient identifiers are pseudonymized so that health data from the same patient collected across various sources can still be linked without compromising the deidentification. Results: The Healthdata.be platform currently collects data for >150 clinical registries in Belgium. We demonstrated how the data collection for the Belgian primary care morbidity register INTEGO is organized and how the Healthdata.be platform can be used for a cluster randomized trial. Conclusions: Collecting health data in various sources and linking these data to a single patient is a promising feature that can potentially address important concerns on the validity and quality of health data. Safe methods of data transfer without compromising privacy are capable of transporting these data from the primary data provider or clinician to a research facility. More research is required to demonstrate that these methods improve the quality of data collection, allowing researchers to rely on electronic health records as a valid source for scientific data.

  • An integrated decision support software for gay and bisexual men attending general practice (montage). Source: The Authors / Placeit; Copyright: JMIR Publications; URL: http://medinform.jmir.org/2018/4/e10808/; License: Creative Commons Attribution (CC-BY).
    Decision Support for Health...

    Nov 6, 2018

    Assessing the Impacts of Integrated Decision Support Software on Sexual Orientation Recording, Comprehensive Sexual Health Testing, and Detection of...

    Authors List:

    Abstract:

    Background: Gay and bisexual men are disproportionately affected by HIV and other sexually transmissible infections (STIs), yet opportunities for sexual health testing of this population are often missed or incomplete in general practice settings. Strategies are needed for improving the uptake and completeness of sexual health testing in this setting. Objectives: The goal of the research was to evaluate the impact of an intervention centered around integrated decision support software and routine data feedback on the collection of sexual orientation data and sexual health testing among gay and bisexual men attending general practice. Methods: A study using before/after and intervention/comparison methods was undertaken to assess the intervention’s impact in 7 purposively sampled Australian general practice clinics located near the urban centers of Sydney and Melbourne. The software was introduced at staggered points between April and August 2012; it used patient records to prompt clinicians to record sexual orientation and accessed pathology testing history to generate prompts when sexual health testing was overdue or incomplete. The software also had a function for querying patient management system databases in order to generate de-identified data extracts, which were used to report regularly to participating clinicians. We calculated summary rate ratios (SRRs) based on quarterly trends and used Poisson regression analyses to assess differences between the 12-month preintervention and 24-month intervention periods as well as between the intervention sites and 4 similar comparison sites that did not receive the intervention. Results: Among 32,276 male patients attending intervention clinics, sexual orientation recording increased 19% (from 3213/6909 [46.50%] to 5136/9110 [56.38%]) during the intervention period (SRR 1.10, 95% CI 1.04-1.11, P<.001) while comprehensive sexual health testing increased by 89% (305/1159 [26.32%] to 690/1413 [48.83%]; SRR 1.38, 95% CI 1.28-1.46, P<.001). Comprehensive testing increased slightly among the 7290 gay and bisexual men attending comparison sites, but the increase was comparatively greater in clinics that received the intervention (SRR 1.12, 95% CI 1.10-1.14, P<.001). In clinics that received the intervention, there was also an increase in detection of chlamydia and gonorrhea that was not observed in the comparison sites. Conclusions: Integrated decision support software and data feedback were associated with modest increases in sexual orientation recording, comprehensive testing among gay and bisexual men, and the detection of STIs. Tests for and detection of chlamydia and gonorrhea were the most dramatically impacted. Decision support software can be used to enhance the delivery of sexual health care in general practice.

  • Source: Pixabay; Copyright: Sasin Tipchai; URL: https://pixabay.com/en/clinic-medical-health-care-disease-1807543/; License: Public Domain (CC0).
    Information Models

    Nov 5, 2018

    Identifying Patients Who Are Likely to Receive Most of Their Care From a Specific Health Care System: Demonstration via Secondary Analysis

    Authors List:

    Abstract:

    Background: In the United States, health care is fragmented in numerous distinct health care systems including private, public, and federal organizations like private physician groups and academic medical centers. Many patients have their complete medical data scattered across these several health care systems, with no particular system having complete data on any of them. Several major data analysis tasks such as predictive modeling using historical data are considered impractical on incomplete data. Objective: Our objective was to find a way to enable these analysis tasks for a health care system with incomplete data on many of its patients. Methods: This study presents, to the best of our knowledge, the first method to use a geographic constraint to identify a reasonably large subset of patients who tend to receive most of their care from a given health care system. A data analysis task needing relatively complete data can be conducted on this subset of patients. We demonstrated our method using data from the University of Washington Medicine (UWM) and PreManage data covering the use of all hospitals in Washington State. We compared 10 candidate constraints to optimize the solution. Results: For UWM, the best constraint is that the patient has a UWM primary care physician and lives within 5 miles of at least one UWM hospital. About 16.01% (55,707/348,054) of UWM patients satisfied this constraint. Around 69.38% (10,501/15,135) of their inpatient stays and emergency department visits occurred within UWM in the following 6 months, more than double the corresponding percentage for all UWM patients. Conclusions: Our method can identify a reasonably large subset of patients who tend to receive most of their care from UWM. This enables several major analysis tasks on incomplete medical data that were previously deemed infeasible.

  • Emergency department. Source: FEMA; Copyright: Robert Kaufmann / FEMA; URL: https://www.fema.gov/media-library/assets/images/47581; License: Public Domain (CC0).
    Secondary Use of Clinical Data for...

    Nov 5, 2018

    Appropriateness of Hospital Admission for Emergency Department Patients with Bronchiolitis: Secondary Analysis

    Authors List:

    Abstract:

    Background: Bronchiolitis is the leading cause of hospitalization in children under 2 years of age. Each year in the United States, bronchiolitis results in 287,000 emergency department visits, 32%-40% of which end in hospitalization. Frequently, emergency department disposition decisions (to discharge or hospitalize) are made subjectively because of the lack of evidence and objective criteria for bronchiolitis management, leading to significant practice variation, wasted health care use, and suboptimal outcomes. At present, no operational definition of appropriate hospital admission for emergency department patients with bronchiolitis exists. Yet, such a definition is essential for assessing care quality and building a predictive model to guide and standardize disposition decisions. Our prior work provided a framework of such a definition using 2 concepts, one on safe versus unsafe discharge and another on necessary versus unnecessary hospitalization. Objective: The goal of this study was to determine the 2 threshold values used in the 2 concepts, with 1 value per concept. Methods: Using Intermountain Healthcare data from 2005-2014, we examined distributions of several relevant attributes of emergency department visits by children under 2 years of age for bronchiolitis. Via a data-driven approach, we determined the 2 threshold values. Results: We completed the first operational definition of appropriate hospital admission for emergency department patients with bronchiolitis. Appropriate hospital admissions include actual admissions with exposure to major medical interventions for more than 6 hours, as well as actual emergency department discharges, followed by an emergency department return within 12 hours ending in admission for bronchiolitis. Based on the definition, 0.96% (221/23,125) of the emergency department discharges were deemed unsafe. Moreover, 14.36% (432/3008) of the hospital admissions from the emergency department were deemed unnecessary. Conclusions: Our operational definition can define the prediction target for building a predictive model to guide and improve emergency department disposition decisions for bronchiolitis in the future.

  • Paper vital sign records. Source: Pixabay; Copyright: rawpixel; URL: https://pixabay.com/en/woman-person-desktop-work-aerial-3187087/; License: Public Domain (CC0).
    Electronic Health Records

    Oct 31, 2018

    Impact of Electronic Versus Paper Vital Sign Observations on Length of Stay in Trauma Patients: Stepped-Wedge, Cluster Randomized Controlled Trial

    Authors List:

    Abstract:

    Background: Electronic recording of vital sign observations (e-Obs) has become increasingly prevalent in hospital care. The evidence of clinical impact for these systems is mixed. Objective: The objective of our study was to assess the effect of e-Obs versus paper documentation (paper) on length of stay (time between trauma unit admission and “fit to discharge”) for trauma patients. Methods: A single-center, randomized stepped-wedge study of e-Obs against paper was conducted in two 26-bed trauma wards at a medium-sized UK teaching hospital. Randomization of the phased intervention order to 12 study areas was computer generated. The primary outcome was length of stay. Results: A total of 1232 patient episodes were randomized (paper: 628, e-Obs: 604). There were 37 deaths in hospital: 21 in the paper arm and 16 in the e-Obs arm. For discharged patients, the median length of stay was 5.4 (range: 0.2-79.0) days on the paper arm and 5.6 (range: 0.1-236.7) days on the e-Obs arm. Competing risks regression analysis for time to discharge showed no difference between the treatment arms (subhazard ratio: 1.05; 95% CI 0.82-1.35; P=.68). A greater proportion of patient episodes contained an Early Warning Score (EWS) ≥3 using the e-Obs system than using paper (subhazard ratio: 1.63; 95% CI 1.28-2.09; P<.001). However, there was no difference in the time to the subsequent observation, “escalation time” (hazard ratio 1.05; 95% CI 0.80-1.38; P=.70). Conclusions: The phased introduction of an e-Obs documentation system was not associated with a change in length of stay. A greater proportion of patient episodes contained an EWS≥3 using the e-Obs system, but this was not associated with a change in “escalation time.” Trial Registration: ISRCTN Registry ISRCTN91040762; http://www.isrctn.com/ISRCTN91040762 (Archived by WebCite at http://www.webcitation.org/72prakGTU)

  • Source: Pixabay; Copyright: Riala; URL: https://pixabay.com/en/girl-person-youth-telephone-hands-1103952/; License: Public Domain (CC0).
    Clinical Communication, Electronic...

    Oct 30, 2018

    Benefits and Costs of Digital Consulting in Clinics Serving Young People With Long-Term Conditions: Mixed-Methods Approach

    Authors List:

    Abstract:

    Background: Since the introduction of digital health technologies in National Health Service (NHS), health professionals are starting to use email, text, and other digital methods to consult with their patients in a timely manner. There is lack of evidence regarding the economic impact of digital consulting in the United Kingdom (UK) NHS. Objective: This study aimed to estimate the direct costs associated with digital consulting as an adjunct to routine care at 18 clinics serving young people aged 16-24 years with long-term conditions. Methods: This study uses both quantitative and qualitative approaches. Semistructured interviews were conducted with 173 clinical team members on the impacts of digital consulting. A structured questionnaire was developed and used for 115 health professionals across 12 health conditions at 18 sites in the United Kingdom to collect data on time and other resources used for digital consulting. A follow-up semistructured interview was conducted with a single senior clinician at each site to clarify the mechanisms through which digital consulting use might lead to outcomes relevant to economic evaluation. We used the two-part model to see the association between the time spent on digital consulting and the job role of staff, type of clinic, and the average length of the working hours using digital consulting. Results: When estimated using the two-part model, consultants spent less time on digital consulting compared with nurses (95.48 minutes; P<.001), physiotherapists (55.3 minutes; P<.001), and psychologists (31.67 minutes; P<.001). Part-time staff spent less time using digital consulting than full-time staff despite insignificant result (P=.15). Time spent on digital consulting differed across sites, and no clear pattern in using digital consulting was found. Health professionals qualitatively identified the following 4 potential economic impacts for the NHS: decreasing adverse events, improving patient well-being, decreasing wait lists, and staff workload. We did not find evidence to suggest that the clinical condition was associated with digital consulting use. Conclusions: Nurses and physiotherapists were the greatest users of digital consulting. Teams appear to use an efficient triage system with the most expensive members digitally consulting less than lower-paid team members. Staff report showed concerns regarding time spent digitally consulting, which implies that direct costs increase. There remain considerable gaps in evidence related to cost-effectiveness of digital consulting, but this study has highlighted important cost-related outcomes for assessment in future cost-effectiveness trials of digital consulting.

  • Source: Flickr; Copyright: ILO in Asia and the Pacific; URL: https://www.flickr.com/photos/iloasiapacific/12159165193; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).
    Imaging Informatics

    Oct 25, 2018

    A Computerized Method for Measuring Computed Tomography Pulmonary Angiography Yield in the Emergency Department: Validation Study

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    Abstract:

    Background: Use of computed tomography pulmonary angiography (CTPA) in the assessment of pulmonary embolism (PE) has markedly increased over the past two decades. While this technology has improved the accuracy of radiological testing for PE, CTPA also carries the risk of substantial iatrogenic harm. Each CTPA carries a 14% risk of contrast-induced nephropathy and a lifetime malignancy risk that can be as high as 2.76%. The appropriate use of CTPA can be estimated by monitoring the CTPA yield, the percentage of tests positive for PE. This is the first study to propose and validate a computerized method for measuring the CTPA yield in the emergency department (ED). Objective: The objective of our study was to assess the validity of a novel computerized method of calculating the CTPA yield in the ED. Methods: The electronic health record databases at two tertiary care academic hospitals were queried for CTPA orders completed in the ED over 1-month periods. These visits were linked with an inpatient admission with a discharge diagnosis of PE based on the International Classification of Diseases codes. The computerized the CTPA yield was calculated as the number of CTPA orders with an associated inpatient discharge diagnosis of PE divided by the total number of orders for completed CTPA. This computerized method was then validated by 2 independent reviewers performing a manual chart review, which included reading the free-text radiology reports for each CTPA. Results: A total of 349 CTPA orders were completed during the 1-month periods at the two institutions. Of them, acute PE was diagnosed on CTPA in 28 studies, with a CTPA yield of 7.7%. The computerized method correctly identified 27 of 28 scans positive for PE. The one discordant scan was tied to a patient who was discharged directly from the ED and, as a result, never received an inpatient discharge diagnosis. Conclusions: This is the first successful validation study of a computerized method for calculating the CTPA yield in the ED. This method for data extraction allows for an accurate determination of the CTPA yield and is more efficient than manual chart review. With this ability, health care systems can monitor the appropriate use of CTPA and the effect of interventions to reduce overuse and decrease preventable iatrogenic harm.

  • Developer testing with Crucible and Touchstone. Source: Image created by the Authors; Copyright: The Authors; URL: http://medinform.jmir.org/2018/4/e10870/; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).
    Electronic Health Records

    Oct 23, 2018

    Validation and Testing of Fast Healthcare Interoperability Resources Standards Compliance: Data Analysis

    Authors List:

    Abstract:

    Background: There is wide recognition that the lack of health data interoperability has significant impacts. Traditionally, health data standards are complex and test-driven methods played important roles in achieving interoperability. The Health Level Seven International (HL7) standard Fast Healthcare Interoperability Resources (FHIR) may be a technical solution that aligns with policy, but systems need to be validated and tested. Objective: Our objective is to explore the question of whether or not the regular use of validation and testing tools improves server compliance with the HL7 FHIR specification. Methods: We used two independent validation and testing tools, Crucible and Touchstone, and analyzed the usage and result data to determine their impact on server compliance with the HL7 FHIR specification. Results: The use of validation and testing tools such as Crucible and Touchstone are strongly correlated with increased compliance and “practice makes perfect.” Frequent and thorough testing has clear implications for health data interoperability. Additional data analysis reveals trends over time with respect to vendors, use cases, and FHIR versions. Conclusions: Validation and testing tools can aid in the transition to an interoperable health care infrastructure. Developers that use testing and validation tools tend to produce more compliant FHIR implementations. When it comes to health data interoperability, “practice makes perfect.”

  • Screenshot of clinician facing version of COMPASS eHealth platform. Source: The Authors; Copyright: The Authors; URL: http://medinform.jmir.org/2018/4/e46; License: Licensed by JMIR.
    eHealth Infrastructures

    Oct 22, 2018

    Development of an eHealth System to Capture and Analyze Patient Sensor and Self-Report Data: Mixed-Methods Assessment of Potential Applications to Improve...

    Authors List:

    Abstract:

    Background: Capturing and Analyzing Sensor and Self-Report Data for Clinicians and Researchers (COMPASS) is an electronic health (eHealth) platform designed to improve cancer care delivery through passive monitoring of patients’ health status and delivering customizable reports to clinicians. Based on data from sensors and context-driven administration of patient-reported outcome (PRO) measures, key indices of patients’ functional status can be collected between regular clinic visits, supporting clinicians in the delivery of patient care. Objective: The first phase of this project aimed to systematically collect input from oncology providers and patients on potential clinical applications for COMPASS to refine the system. Methods: Ten clinicians representing various oncology specialties and disciplines completed semi-structured interviews designed to solicit clinician input on how COMPASS can best support clinical care delivery. Three cancer patients tested a prototype of COMPASS for 7 days and provided feedback. Interview data were tabulated using thematic content analysis to identify the most clinically relevant objective and PRO domains. Results: Thematic content analysis revealed that clinicians were most interested in monitoring vital statistics, symptoms, and functional status, including the physical activity level (n=9), weight (n=5), fatigue (n=9), sleep quality (n=8), and anxiety (n=7). Patients (2 in active treatment and 1 in remission) reported that they would use such a device, were enthusiastic about their clinicians monitoring their health status, especially the tracking of symptoms, and felt knowing their clinicians were monitoring and reviewing their health status provided valuable reassurance. Patients would, however, like to provide some context to their data. Conclusions: Clinicians and patients both articulated potential benefits of the COMPASS system in improving cancer care. From a clinician standpoint, data need to be easily interpretable and actionable. The fact that patients and clinicians both see potential value in eHealth systems suggests wider adoption and utilization could prove to be a useful tool for improving care delivery.

  • A happy phlebotomy patient. Source: Flickr; Copyright: Tri Nguyen; URL: https://www.flickr.com/photos/bao_tri_nguyen/7749571366; License: Creative Commons Attribution (CC-BY).
    Decision Support for Health...

    Oct 22, 2018

    Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record

    Authors List:

    Abstract:

    Background: Electronic, personalized clinical decision support tools to optimize glycated hemoglobin (HbA1c) screening are lacking. Current screening guidelines are based on simple, categorical rules developed for populations of patients. Although personalized diabetes risk calculators have been created, none are designed to predict current glycemic status using structured data commonly available in electronic health records (EHRs). Objective: The goal of this project was to create a mathematical equation for predicting the probability of current elevations in HbA1c (≥5.7%) among patients with no history of hyperglycemia using readily available variables that will allow integration with EHR systems. Methods: The reduced model was compared head-to-head with calculators created by Baan and Griffin. Ten-fold cross-validation was used to calculate the bias-adjusted prediction accuracy of the new model. Statistical analyses were performed in R version 3.2.5 (The R Foundation for Statistical Computing) using the rms (Regression Modeling Strategies) package. Results: The final model to predict an elevated HbA1c based on 22,635 patient records contained the following variables in order from most to least importance according to their impact on the discriminating accuracy of the model: age, body mass index, random glucose, race, serum non–high-density lipoprotein, serum total cholesterol, estimated glomerular filtration rate, and smoking status. The new model achieved a concordance statistic of 0.77 which was statistically significantly better than prior models. The model appeared to be well calibrated according to a plot of the predicted probabilities versus the prevalence of the outcome at different probabilities. Conclusions: The calculator created for predicting the probability of having an elevated HbA1c significantly outperformed the existing calculators. The personalized prediction model presented in this paper could improve the efficiency of HbA1c screening initiatives.

  • Source: Pixnio; Copyright: Pixnio; URL: https://pixnio.com/objects/computer/laptop-computer-internet-marketing-programming-programmer-typing-writing-computer-blogging-work#; License: Public Domain (CC0).
    Decision Support for Health...

    Oct 10, 2018

    Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering...

    Authors List:

    Abstract:

    Background: In the United States, a rare disease is characterized as the one affecting no more than 200,000 patients at a certain period. Patients suffering from rare diseases are often either misdiagnosed or left undiagnosed, possibly due to insufficient knowledge or experience with the rare disease on the part of clinical practitioners. With an exponentially growing volume of electronically accessible medical data, a large volume of information on thousands of rare diseases and their potentially associated diagnostic information is buried in electronic medical records (EMRs) and medical literature. Objective: This study aimed to leverage information contained in heterogeneous datasets to assist rare disease diagnosis. Phenotypic information of patients existed in EMRs and biomedical literature could be fully leveraged to speed up diagnosis of diseases. Methods: In our previous work, we advanced the use of a collaborative filtering recommendation system to support rare disease diagnostic decision making based on phenotypes derived solely from EMR data. However, the influence of using heterogeneous data with collaborative filtering was not discussed, which is an essential problem while facing large volumes of data from various resources. In this study, to further investigate the performance of collaborative filtering on heterogeneous datasets, we studied EMR data generated at Mayo Clinic as well as published article abstracts retrieved from the Semantic MEDLINE Database. Specifically, in this study, we designed different data fusion strategies from heterogeneous resources and integrated them with the collaborative filtering model. Results: We evaluated performance of the proposed system using characterizations derived from various combinations of EMR data and literature, as well as with sole EMR data. We extracted nearly 13 million EMRs from the patient cohort generated between 2010 and 2015 at Mayo Clinic and retrieved all article abstracts from the semistructured Semantic MEDLINE Database that were published till the end of 2016. We applied a collaborative filtering model and compared the performance generated by different metrics. Log likelihood ratio similarity combined with k-nearest neighbor on heterogeneous datasets showed the optimal performance in patient recommendation with area under the precision-recall curve (PRAUC) 0.475 (string match), 0.511 (systematized nomenclature of medicine [SNOMED] match), and 0.752 (Genetic and Rare Diseases Information Center [GARD] match). Log likelihood ratio similarity also performed the best with mean average precision 0.465 (string match), 0.5 (SNOMED match), and 0.749 (GARD match). Performance of rare disease prediction was also demonstrated by using the optimal algorithm. Macro-average F-measure for string, SNOMED, and GARD match were 0.32, 0.42, and 0.63, respectively. Conclusions: This study demonstrated potential utilization of heterogeneous datasets in a collaborative filtering model to support rare disease diagnosis. In addition to phenotypic-based analysis, in the future, we plan to further resolve the heterogeneity issue and reduce miscommunication between EMR and literature by mining genotypic information to establish a comprehensive disease-phenotype-gene network for rare disease diagnosis.

  • A patient receiving instructions from their pharmacist. Source: Image created by the Authors; Copyright: The Authors; URL: http://medinform.jmir.org/2018/3/e11021/; License: Creative Commons Attribution (CC-BY).
    Pharmacovigilance and Surveillance

    Sep 27, 2018

    Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in...

    Authors List:

    Abstract:

    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.

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  • Evaluation of Nursing Information System Implementation in two General Hospitals Affiliated to Zahedan University of Medical Sciences in Southeast Iran: Nurses’ Viewpoints

    Date Submitted: Nov 4, 2018

    Open Peer Review Period: Nov 8, 2018 - Jan 3, 2019

    Background: The use of NIS capabilities depends on the needs of users. Also, the proper design of these systems disrupts the daily processes of the users and complicates the acceptance of these system...

    Background: The use of NIS capabilities depends on the needs of users. Also, the proper design of these systems disrupts the daily processes of the users and complicates the acceptance of these systems. Objective: the purpose of this study was to evaluate nurses’ perceptions about the effectiveness of the NIS system and its impact on their activities. Methods: This cross-sectional survey was conducted in 2017. The research population consisted of 656 nurses working in two general hospitals affiliated to Zahedan University of Medical Sciences. According to the Cochran formula, 346 people were needed and a random stratified sampling was used to select the sample in each hospital. Data collection tool, model and questionnaire designed by Hung-Hsiou Hsu et al. The questionnaire consisted of two parts: the first part contained demographic information, and the second part was designed to collect nursing staff's views on the implementation of the NIS system. Validity of the questionnaire was verified by five experts. To determine the reliability of the questionnaire, a re-validation test was used. The Cronbach's alpha of the questionnaire was 0.92. Data analysis was done using SPSS.v22 software. Results: The highest and lowest mean scores of nurses' perceptions related to the ease of use perceived and user satisfaction with the score of 3.55 ± 67 and 3.33 ± 1.39 respectively. According to the regression test, the quality of information and service quality have a positive effect on the perceived ease of use and perceived usefulness of the NIS. And the quality of the system does not affect the perceived usefulness of the NIS. The perceived usefulness of using NIS has a positive and significant impact on users' willingness to use this system and user satisfaction over their intention to use of NIS. Conclusions: The lack of complete satisfaction of NIS users in this study could be due to the lack of user-friendliness, ease of use, and the inability to receive timely information for nurses. One of the obvious weaknesses of this system is from the viewpoint of nurses that there are unnecessary items that somehow slow down the work process. It seems that the needs of users in the design or purchase of NIS are not completely covered and new requirements are not taken into account during the use of this system.

  • Implementation of Computed-Aided Detection for Breast Cancer Screening in Clinical Settings: A Scoping Review

    Date Submitted: Oct 31, 2018

    Open Peer Review Period: Nov 3, 2018 - Dec 29, 2018

    Background: With the growth of machine learning applications, the practice of medicine is evolving. Computer-Aided Detection (CAD) is a software technology which has become widespread in radiology pra...

    Background: With the growth of machine learning applications, the practice of medicine is evolving. Computer-Aided Detection (CAD) is a software technology which has become widespread in radiology practices, particularly in breast cancer screening to improve detection rates at earlier stages. Many studies have investigated the diagnostic accuracy of CAD but its implementation in clinical settings has been largely overlooked. Objective: The aim of this scoping review was to summarize recent literature on the adoption and implementation of CAD during breast cancer screening by radiologists and to describe barriers and facilitators for CAD use. Methods: MEDLINE was searched for English, peer-reviewed articles that described CAD implementation, including barriers or facilitators, in breast cancer screening, published between 2010 to March 2018. Articles describing the diagnostic accuracy of CAD for breast cancer detection were excluded. The search returned 526 citations which were reviewed in duplicate through abstract and full text screening. Reference lists and cited references of included studies were reviewed. Results: 9 articles met the inclusion criteria. The included literature showed that there is a tradeoff between facilitators and barriers for CAD use. Facilitators for CAD use were improved breast cancer detection rates, increased profitability of breast imaging, and time saved by replacing double reading. Identified barriers were less favorable perceptions of CAD compared to double reading by radiologists, an increase in recall rates of patients for further testing, increased costs, and unclear effect on patient outcomes. Conclusions: There is a gap in the literature between CAD’s well-established diagnostic accuracy and its implementation and use by radiologists. Generally, the perceptions of radiologists have not been considered and details of implementation approaches for adoption of CAD have not been reported. Cost-effectiveness of CAD has not been well established for breast cancer screening in various populations. Further research is needed on how to best facilitate CAD in radiology practices to optimize patient outcomes, and the views of radiologists need to be better incorporated along with advancing CAD use.

  • An Expert System for the Prognostication of the Brain and Nerve Diseases in Children with Convulsion Signs Based on Certainty Factors

    Date Submitted: Oct 29, 2018

    Open Peer Review Period: Nov 3, 2018 - Dec 29, 2018

    Background: Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of...

    Background: Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. Objective: Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. Methods: Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. Results: Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. Conclusions: Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors.

  • An assessment of the interoperability of electronic health record exchanges among hospitals and clinics in Taiwan

    Date Submitted: Oct 29, 2018

    Open Peer Review Period: Nov 3, 2018 - Dec 29, 2018

    Background: The rapid aging of the Taiwanese population has led to high medical needs for the elderly and increasing medical costs in recent years. The integration of patient information through elect...

    Background: The rapid aging of the Taiwanese population has led to high medical needs for the elderly and increasing medical costs in recent years. The integration of patient information through electronic health records (EHRs) to reduce unnecessary medications and tests and enhance the quality of care has currently become an important issue. Objective: Although electronic data interchanges among hospitals and clinics have been implemented for many years in Taiwan, the interoperability of EHRs has still not been assessed. Hence, in this study, we analyzed the efficiency of data exchanges and provide suggestions for future improvements. Methods: We obtained 30 months of uploaded and downloaded data of EHRs among hospitals and clinics from the EMR Exchange Center (EEC) of the Ministry of Health and Welfare (MOHW) from January 2015 to June 2017. The objects of this study consisted of 21 medical centers, 84 district hospitals, 290 area hospitals and 5520 clinics. The four exchange EHR forms examined were blood tests, medical images, discharge summaries, and outpatient medical records. We used MYSQL software to save our data and used phpMyAdmin which is a PHP program to manage the database and then analyzed the data using SPSS 19.0 statistical software. Results: The quarterly mean uploaded volume of EHRs among hospitals was 52,790,721 (standard deviation (SD): 580,643) records. The quarterly mean downloaded volume of EHRs among hospitals and clinics was 650,323 (SD: 215,099) records. The ratio of uploaded to downloaded EHRs was about 81:1. The total volume of EHRs was mainly downloaded by medical centers and clinics which accounted for 53.8% (mean: 318,717.80) and 45.4% (mean: 269,082.10), respectively, and the statistical test was significant among different hospital accreditation levels (F=7.63, p<0.001). A comparison of EHR download volumes among the six National Health Insurance (NHI) branches showed that the central NHI branch downloaded 11,366,431 records (21.53%) which was the highest, and the eastern branch downloaded 1,615,391 records (3.06%) which was the lowest, and the statistical test among the six NHI branches was significant (F=8.82, p<0.001). The download volumes of laboratory tests and outpatient medical records were 26,980,425 (50.3%) and 21,747,588 records (40.9%), respectively, and were much higher than medical images and discharge summaries, and the statistical test was also significant (F=17.72, p<0.001). Finally, the download times showed that the average for x-rays was 32.05 seconds which was the longest, and 9.92 seconds for electrocardiograms which was the shortest, but there was no statistically significant difference among download times for various medical images. Conclusions: After years of operation, the EEC has achieved the initial goal of EHR interoperability, and data exchanges are running quite stably in Taiwan. However, the meaningful use of EHRs among hospitals and clinics still needs further encouragement and promotion. We suggest that the government’s leading role and collective collaboration with healthcare organizations are important keys to providing effective health information exchanges. Clinical Trial: MOHW105-IM-I-114-000017 (Department of Health and Welfare)

  • Medication Adherence Prediction from Social Forums with Transfer Learning

    Date Submitted: Oct 20, 2018

    Open Peer Review Period: Oct 25, 2018 - Dec 20, 2018

    Background: Medication non-adherence and non-compliance left unaddressed can compound into severe medical problems for patients. Identifying patients that are likely to become non-adherent can help re...

    Background: Medication non-adherence and non-compliance left unaddressed can compound into severe medical problems for patients. Identifying patients that are likely to become non-adherent can help reduce these problems. Despite these benefits, monitoring adherence at scale is cost-prohibitive. Social forums offer an easily accessible, affordable, and timely alternative to the traditional methods based on claims data. Objective: This study investigates the potential of medication adherence prediction based on social forum data for diabetes and fibromyalgia therapies by using transfer learning from the Medical Expenditure Panel Survey (MEPS). Methods: Predictive adherence models are developed by using both survey and social forums data and different random forest (RF) techniques. The first of these techniques uses binned input from k-means clustering. The second technique is based on ternary trees instead of the widely used binary decision trees. These techniques are able to handle missing data, a prevalent characteristic of social forums. Results: The results of this study show that transfer learning between survey models and social forum models is possible. Using MEPS survey data and the techniques listed above to derive RF models, less than 5% difference in accuracy was observed between the MEPS test dataset and the social forum test dataset. These models also helped identify the most important predictive features for medication adherence in the social forum data. Conclusions: The proposed approach can help support the study of medication adherence at scale. This work shows that a model trained using verified survey data can be used to complement traditional medical adherence models by predicting adherence from unverified, self-reported data in a dynamic and timely manner. Furthermore, this model provides a method for discovering objective insights from subjective social reports. Additional investigation is needed to improve the prediction accuracy of the proposed model and to assess biases that may be inherent to self-reported adherence measures in social health networks.

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