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Journal Description

JMIR Medical Informatics (JMI, ISSN 2291-9694) (Editor-in-chief: Christian Lovis MD MPH FACMI) is a Pubmed/SCIE-indexed, top-rated, tier A journal with impact factor expected in 2019, 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 2018: 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 is indexed in PubMed Central/PubMed, and has also been accepted for SCIE, with an official Clarivate impact factor 2018 expected to be released in 2019 (see announcement).

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 (


Recent Articles:

  • A rehabilitated child at the Children's Hospital of Shanghai. Source: Image created by the Authors; Copyright: Children's Hospital of Shanghai; URL:; License: Creative Commons Attribution (CC-BY).

    Medication Use for Childhood Pneumonia at a Children’s Hospital in Shanghai, China: Analysis of Pattern Mining Algorithms


    Background: Pattern mining utilizes multiple algorithms to explore objective and sometimes unexpected patterns in real-world data. This technique could be applied to electronic medical record data mining; however, it first requires a careful clinical assessment and validation. Objective: The aim of this study was to examine the use of pattern mining techniques on a large clinical dataset to detect treatment and medication use patterns for childhood pneumonia. Methods: We applied 3 pattern mining algorithms to 680,138 medication administration records from 30,512 childhood inpatients with diagnosis of pneumonia during a 6-year period at a children’s hospital in China. Patients’ ages ranged from 0 to 17 years, where 37.53% (11,453/30,512) were 0 to 3 months old, 86.55% (26,408/30,512) were under 5 years, 60.37% (18,419/30,512) were male, and 60.10% (18,338/30,512) had a hospital stay of 9 to 15 days. We used the FP-Growth, PrefixSpan, and USpan pattern mining algorithms. The first 2 are more traditional methods of pattern mining and mine a complete set of frequent medication use patterns. PrefixSpan also incorporates an administration sequence. The newer USpan method considers medication utility, defined by the dose, frequency, and timing of use of the 652 individual medications in the dataset. Together, these 3 methods identified the top 10 patterns from 6 age groups, forming a total of 180 distinct medication combinations. These medications encompassed the top 40 (73.66%, 500,982/680,138) most frequently used medications. These patterns were then evaluated by subject matter experts to summarize 5 medication use and 2 treatment patterns. Results: We identified 5 medication use patterns: (1) antiasthmatics and expectorants and corticosteroids, (2) antibiotics and (antiasthmatics or expectorants or corticosteroids), (3) third-generation cephalosporin antibiotics with (or followed by) traditional antibiotics, (4) antibiotics and (medications for enteritis or skin diseases), and (5) (antiasthmatics or expectorants or corticosteroids) and (medications for enteritis or skin diseases). We also identified 2 frequent treatment patterns: (1) 42.89% (291,701/680,138) of specific medication administration records were of intravenous therapy with antibiotics, diluents, and nutritional supplements and (2) 11.53% (78,390/680,138) were of various combinations of inhalation of antiasthmatics, expectorants, or corticosteroids. Fleiss kappa for the subject experts’ evaluation was 0.693, indicating moderate agreement. Conclusions: Utilizing a pattern mining approach, we summarized 5 medication use patterns and 2 treatment patterns. These warrant further investigation.

  • Tooth extraction sockets. Source: Shutterstock Inc; Copyright: cl2004lhy; URL:; License: Licensed by the authors.

    Completeness of Electronic Dental Records in a Student Clinic: Retrospective Analysis


    Background: A well-designed, adequately documented, and properly maintained patient record is an important tool for quality assurance and care continuity. Good clinical documentation skills are supposed to be a fundamental part of dental student training. Objective: The goal of this study was to assess the completeness of electronic patient records in a student clinic. Methods: Completeness of patient records was assessed using comparative review of validated cases of alveolar osteitis treated between August 2011 and May 2017 in a student clinic at Columbia University College of Dental Medicine, New York, USA. Based on a literature review, population-based prevalence of nine most frequently mentioned symptoms, signs, and treatment procedures of alveolar osteitis was identified. Completeness of alveolar osteitis records was assessed by comparison of population-based prevalence and frequency of corresponding items in the student documentation. To obtain all alveolar osteitis cases, we ran a query on the electronic dental record, which included all cases with diagnostic code Z1820 or any variation of the phrases “dry socket” and “alveolar osteitis” in the notes. The resulting records were manually reviewed to definitively confirm alveolar osteitis and to extract all index items. Results: Overall, 296 definitive cases of alveolar osteitis were identified. Only 22% (64/296) of cases contained a diagnostic code. Comparison of the frequency of the nine index categories in the validated alveolar osteitis cases between the student clinic and the population showed the following results: severe pain: 94% (279/296) vs 100% (430/430); bare bone/missing blood clot: 27% (80/296) vs 74% (35/47) to 100% (329/329); malodor: 7% (22/296) vs 33%-50% (18/54); radiating pain to the ear: 8% (24/296) vs 56% (30/54); lymphadenopathy: 1% (3/296) vs 9% (5/54); inflammation: 14% (42/296) vs 50% (27/54); debris: 12% (36/296) vs 87% (47/54); alveolar osteitis site noted: 96% (283/296) vs 100% (430/430; accepted documentation requirement); and anesthesia during debridement: 77% (20/24) vs 100% (430/430; standard of anesthetization prior to debridement). Conclusions: There was a significant discrepancy between the index category frequency in alveolar osteitis cases documented by dental students and in the population (reported in peer-reviewed literature). More attention to clinical documentation skills is warranted in dental student training.

  • Source: Flickr; Copyright: Rebecca Brown; URL:; License: Creative Commons Attribution + Noncommercial (CC-BY-NC).

    Telemonitoring to Manage Chronic Obstructive Pulmonary Disease: Systematic Literature Review


    Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of death throughout the world. Telemedicine has been utilized for many diseases and its prevalence is increasing in the United States. Telemonitoring of patients with COPD has the potential to help patients manage disease and predict exacerbations. Objective: The objective of this review is to evaluate the effectiveness of telemonitoring to manage COPD. Researchers want to determine how telemonitoring has been used to observe COPD and we are hoping this will lead to more research in telemonitoring of this disease. Methods: This review was conducted in accordance with the Assessment for Multiple Systematic Reviews (AMSTAR) and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Authors performed a systematic review of the PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases to obtain relevant articles. Articles were then accepted or rejected by group consensus. Each article was read and authors identified barriers and facilitators to effectiveness of telemonitoring of COPD. Results: Results indicate that conflicting information exists for the effectiveness of telemonitoring of patients with COPD. Primarily, 13 out of 29 (45%) articles stated that patient outcomes were improved overall with telemonitoring, while 11 of 29 (38%) indicated no improvement. Authors identified the following facilitators: reduced need for in-person visits, better disease management, and bolstered patient-provider relationship. Important barriers included low-quality data, increased workload for providers, and cost. Conclusions: The high variability between the articles and the ways they provided telemonitoring services created conflicting results from the literature review. Future research should emphasize standardization of telemonitoring services and predictability of exacerbations.

  • Source: Pexels; Copyright: katemangostar; URL:; License: Licensed by JMIR.

    Understanding Determinants of Health Care Professionals’ Perspectives on Mobile Health Continuance and Performance


    Background: With the widespread use of mobile technologies, mobile information systems have become crucial tools in health care operations. Although the appropriate use of mobile health (mHealth) may result in major advances in expanding health care coverage (increasing decision-making speeds, managing chronic conditions, and providing suitable health care in emergencies), previous studies have argued that current mHealth research does not adequately evaluate mHealth interventions, and it does not provide sufficient evidence regarding the effects on health. Objective: The aim of this study was to facilitate the widespread use of mHealth systems; an accurate evaluation of the systems from the users’ perspective is essential after the implementation and use of the system in daily health care practices. This study extends the expectation-confirmation model by using characteristics of individuals, technology, and tasks to identify critical factors affecting mHealth continuance and performance from the perspective of health care professionals (HCPs). Methods: A questionnaire survey was used to collect data from HCPs who were experienced in using mHealth systems of a Taiwanese teaching hospital. In total, 282 questionnaires were distributed, and 201 complete and valid questionnaires were returned, thus indicating a valid response rate of 71.3% (201/282). The collected data were analyzed using WarpPLS version 5.0 (ScriptWarp Systems). Results: The results revealed that mHealth continuance (R2=0.522) was mainly affected by perceived usefulness, technology maturity, individual habits, task mobility, and user satisfaction, whereas individual performance (R2=0.492) was affected by mHealth continuance. In addition, user satisfaction (R2=0.548) was affected by confirmation and perceived usefulness of mHealth, whereas perceived usefulness (R2=0.521) was affected by confirmation. This implied that confirmation played a key role in affecting perceived usefulness and user satisfaction. Furthermore, the results showed that mHealth continuance positively affected individual performance. Conclusions: The identified critical factors influencing mHealth continuance and performance can be used as a useful assessment tool by hospitals that have implemented mHealth systems to facilitate the use and infusion of the systems. Furthermore, the results can help health care institutions that intend to introduce or develop mHealth applications to identify critical issues and effectively allocate limited resources to mHealth systems.

  • Source: Nottingham Universities Hospitals Trust; Copyright: Nottingham Universities Hospitals Trust; URL:; License: Creative Commons Attribution + Noncommercial (CC-BY-NC).

    The Impact of an Electronic Patient Bedside Observation and Handover System on Clinical Practice: Mixed-Methods Evaluation


    Background: Patient safety literature has long reported the need for early recognition of deteriorating patients. Early warning scores (EWSs) are commonly implemented as “track and trigger,” or rapid response systems for monitoring and early recognition of acute patient deterioration. This study presents a human factors evaluation of a hospital-wide transformation in practice, engendered by the deployment of an innovative electronic observations (eObs) and handover system. This technology enables real-time information processing at the patient’s bedside, improves visibility of patient data, and streamlines communication within clinical teams. Objective: The aim of this study was to identify improvement and deterioration in workplace efficiency and quality of care resulting from the large-scale imposition of new technology. Methods: A total of 85 hours of direct structured observations of clinical staff were carried out before and after deployment. We conducted 40 interviews with a range of clinicians. A longitudinal analysis of critical care audit and electronically recorded patient safety incident reports was conducted. The study was undertaken in a large secondary-care facility in the United Kingdom. Results: Roll-out of eObs was associated with approximately 10% reduction in total unplanned admissions to critical care units from eObs-equipped wards. Over time, staff appropriated the technology as a tool for communication, workload management, and improving awareness of team capacity. A negative factor was perceived as lack of engagement with the system by senior clinicians. Doctors spent less time in the office (68.7% to 25.6%). More time was spent at the nurses’ station (6.6% to 41.7%). Patient contact time was more than doubled (2.9% to 7.3%). Conclusions: Since deployment, clinicians have more time for patient care because of reduced time spent inputting and accessing data. The formation of a specialist clinical team to lead the roll-out was universally lauded as the reason for success. Staff valued the technology as a tool for managing workload and identified improved situational awareness as a key benefit. For future technology deployments, the staff requested more training preroll-out, in addition to engagement and support from senior clinicians.

  • Source: Flickr; Copyright: · · · — — — · · ·; URL:; License: Creative Commons Attribution + Noncommercial (CC-BY-NC).

    Implementation of National Health Informatization in China: Survey About the Status Quo


    Background: The National Health and Family Planning Commission (NHFPC) in China organized a nationwide survey to investigate the informatization in hospitals and regional Health and Family Planning Commissions (HFPCs) in 2017. The survey obtained valid results from 79.69% (2021/2536) of major hospitals and 81% (26/32) of provincial and 73.1% (307/420) of municipal HFPCs. The investigated topics covered hardware infrastructure, information resources, applications, systems, and organizations in health informatics. Objective: This study aimed to provide evidence collected from the survey regarding China’s health informatization and assist policy making regarding health informatics in the 13th Five-Year Plan of China. Methods: Based on the survey, the paper presented the status quo of China’s health informatization and analyzed the progress and potential problems in terms of the country’s health information development policies. Results: Related policies have helped to construct 4-level information platforms and start converging the regional data to the 3 centralized databases. The principle of informatics has been transiting from finance-centered to people-centered. Alternatively, the quality, usability, and interoperability of the data still need to be improved. Conclusions: The nationwide survey shows that China’s health informatization is rapidly developing. Current information platforms and databases technically support data exchanging between all provinces and cities. As China is continuing to improve the infrastructure, more advanced applications are being developed upon it.

  • A free clinic for children with precocious puberty, held by the doctors of endocrinology department in Guangzhou Women and Children's Medical Center. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Public Domain (CC0).

    Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study


    Background: Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. Objective: We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. Methods: In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. Results: Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. Conclusions: The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

  • Source: Air Force Medical Service (Malcolm Mayfield); Copyright: US Air Force; URL:; License: Public Domain (CC0).

    Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders:...


    Background: Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance. Objective: We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event. Methods: We expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data. Results: HCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models. Conclusions: By incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events.

  • Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study


    Background: Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR–based clinical decision support (CDS), shedding light on whether and why CDS is effective. Objective: This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians’ (PCPs’) opening of and response to noninterruptive alerts delivered to EHR InBaskets. Methods: We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs’ InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients’ posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs’ follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. Results: We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. Conclusions: EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR–based study.

  • Source: Flickr; Copyright: Kanaka Rastamon; URL:; License: Creative Commons Attribution + Noncommercial (CC-BY-NC).

    Data Analysis and Visualization of Newspaper Articles on Thirdhand Smoke: A Topic Modeling Approach


    Background: Thirdhand smoke has been a growing topic for years in China. Thirdhand smoke (THS) consists of residual tobacco smoke pollutants that remain on surfaces and in dust. These pollutants are re-emitted as a gas or react with oxidants and other compounds in the environment to yield secondary pollutants. Objective: Collecting media reports on THS from major media outlets and analyzing this subject using topic modeling can facilitate a better understanding of the role that the media plays in communicating this health issue to the public. Methods: The data were retrieved from the Wiser and Factiva news databases. A preliminary investigation focused on articles dated between January 1, 2013, and December 31, 2017. Use of Latent Dirichlet Allocation yielded the top 10 topics about THS. The use of the modified LDAvis tool enabled an overall view of the topic model, which visualizes different topics as circles. Multidimensional scaling was used to represent the intertopic distances on a two-dimensional plane. Results: We found 745 articles dated between January 1, 2013, and December 31, 2017. The United States ranked first in terms of publications (152 articles on THS from 2013-2017). We found 279 news reports about THS from the Chinese media over the same period and 363 news reports from the United States. Given our analysis of the percentage of news related to THS in China, Topic 1 (Cancer) was the most popular among the topics and was mentioned in 31.9% of all news stories. Topic 2 (Control of quitting smoking) was related to roughly 15% of news items on THS. Conclusions: Data analysis and the visualization of news articles can generate useful information. Our study shows that topic modeling can offer insights into understanding news reports related to THS. This analysis of media trends indicated that related diseases, air and particulate matter (PM2.5), and control and restrictions are the major concerns of the Chinese media reporting on THS. The Chinese press still needs to consider fuller reports on THS based on scientific evidence and with less focus on sensational headlines. We recommend that additional studies be conducted related to sentiment analysis of news data to verify and measure the influence of THS-related topics.

  • Source: Wikimedia Commons; Copyright: Intel Free Press; URL:; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database


    Background: Many intensive care units (ICUs) utilize telemedicine in response to an expanding critical care patient population, off-hours coverage, and intensivist shortages, particularly in rural facilities. Advances in digital health technologies, among other reasons, have led to the integration of active, well-networked critical care telemedicine (tele-ICU) systems across the United States, which in turn, provide the ability to generate large-scale remote monitoring data from critically ill patients. Objective: The objective of this study was to explore opportunities and challenges of utilizing multisite, multimodal data acquired through critical care telemedicine. Using a publicly available tele-ICU, or electronic ICU (eICU), database, we illustrated the quality and potential uses of remote monitoring data, including cohort discovery for secondary research. Methods: Exploratory analyses were performed on the eICU Collaborative Research Database that includes deidentified clinical data collected from adult patients admitted to ICUs between 2014 and 2015. Patient and ICU characteristics, top admission diagnoses, and predictions from clinical scoring systems were extracted and analyzed. Additionally, a case study on respiratory failure patients was conducted to demonstrate research prospects using tele-ICU data. Results: The eICU database spans more than 200 hospitals and over 139,000 ICU patients across the United States with wide-ranging clinical data and diagnoses. Although mixed medical-surgical ICU was the most common critical care setting, patients with cardiovascular conditions accounted for more than 20% of ICU stays, and those with neurological or respiratory illness accounted for nearly 15% of ICU unit stays. The case study on respiratory failure patients showed that cohort discovery using the eICU database can be highly specific, albeit potentially limiting in terms of data provenance and sparsity for certain types of clinical questions. Conclusions: Large-scale remote monitoring data sources, such as the eICU database, have a strong potential to advance the role of critical care telemedicine by serving as a testbed for secondary research as well as for developing and testing tools, including predictive and prescriptive analytical solutions and decision support systems. The resulting tools will also inform coordination of care for critically ill patients, intensivist coverage, and the overall process of critical care telemedicine.

  • Source: Pexels; Copyright:; URL:; License: Licensed by JMIR.

    Information Technology–Assisted Treatment Planning and Performance Assessment for Severe Thalassemia Care in Low- and Middle-Income Countries:...


    Background: Successful models of information and communication technology (ICT) applied to cost-effective delivery of quality care in low- and middle-income countries (LMIC) are an increasing necessity. Severe thalassemia is one of the most common life-threatening noncommunicable diseases of children globally. Objective: The aim was to study the impact of ICT on quality of care for severe thalassemia patients in LMIC. Methods: A total of 1110 patients with severe thalassemia from five centers in India were followed over a 1-year period. The impact of consistent use of a Web-based platform designed to assist comprehensive management of severe thalassemia (ThalCare) on key indicators of quality of care such as minimum (pretransfusion) hemoglobin, serum ferritin, liver size, and spleen size were assessed. Results: Overall improvements in initial hemoglobin, ferritin, and liver and spleen size were significant (P<.001 for each). For four centers, the improvement in mean pretransfusion hemoglobin level was statistically significant (P<.001). Four of five centers achieved reduction in mean ferritin levels, with two displaying a significant drop in ferritin (P=.004 and P<.001). One of the five centers did not record liver and spleen size on palpation, but of the remaining four centers, two witnessed a large drop in liver and spleen size (P<.01), one witnessed moderate drop (P=.05 for liver; P=.03 for spleen size), while the fourth witnessed a moderate increase in liver size (P=.08) and insignificant change in spleen size (P=.12). Conclusions: Implementation of computer-assisted treatment planning and performance assessment consistently and positively impacted indexes reflecting effective delivery of care to patients suffering from severe thalassemia in LMIC.

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  • Monitoring Mental Health Care services using Business Analytics : A Case Study

    Date Submitted: Mar 14, 2019

    Open Peer Review Period: Mar 18, 2019 - May 13, 2019

    Background: Monitoring health care activities is the first step for health stakeholders and health professionals to improve quality and performance of health care services. However, monitoring remains...

    Background: Monitoring health care activities is the first step for health stakeholders and health professionals to improve quality and performance of health care services. However, monitoring remains a challenge for health care facilities especially in developing countries. Fortunately, advances in business analytics address this need. Objective: To demonstrate the use of business analytics descriptive techniques on electronic medical records and to describe business analytics adoption challenges in the University Psychiatric Centre (CPU) of Casablanca, Morocco. Methods: Business analytics descriptive techniques were applied on three years electronic medical records of CPU outpatient consultation. Monitored mental health key metrics concern quantitative assessment of acts, mental health disorder’s frequency, psychiatric drugs prescription, health professional’s performance, patient geolocation and outpatient care wait time. Three annual feedback meetings based on business analytics reports took place. Results: Over the three monitored years, monthly Medical Record Order Entries (MROE) increased significantly. Physicians improved their personal recording. Schizophrenia, depression and bipolar disorders were noted at the top of diagnosis. Antipsychotics are the most prescribed drugs and an average decrease of 12 minutes per year in outpatient care wait time was noted. Conclusions: Business analytics allowed CPU to monitor mental healthcare outpatient activity and to make informed decisions. Yet, challenges should be addressed to step forward in order to become a data-driven health care facility mainly in the organizational dimension of the decision-making process and in the definition of key metrics in response to strategic needs.

  • Weight loss trajectories after obesity/bariatric surgery; a mathematical model satisfactorily classifies 93% of patients.

    Date Submitted: Feb 9, 2019

    Open Peer Review Period: Feb 12, 2019 - Apr 9, 2019

    Background: Obesity surgery has proven its effectiveness in weight loss. However, after a loss phase of about 12-18 months, between 20 and 40% of patients regain weight. Objective: Prediction of weigh...

    Background: Obesity surgery has proven its effectiveness in weight loss. However, after a loss phase of about 12-18 months, between 20 and 40% of patients regain weight. Objective: Prediction of weight evolution is therefore, useful for early detection of weight regain. Methods: This was a monocentric retrospective study with calculation of the weight trajectory of patients having undergone gastric bypass surgery. Data on 795 patients after an interval of 2 years allowed modelling of weight trajectories according to a hierarchical cluster analysis (HCA) tending to minimize the intragroup distance according to Ward. Clinical judgement was used to finalise the identification of clinically relevant representative trajectories. This modelling was validated on a group of 381 patients for whom the observed weight at 18 months was compared to the predicted weight, and the weights were transformed according to Reinhold’s classification of results. Results: Two successive HCA produced 14 representative trajectories, distributed among 4 clinically relevant families: 6 of the 14 weight trajectories decreased systematically over time or decreased, then stagnated; 4 of the 14 trajectories decreased, then increased, then decreased again; 2 of the 14 trajectories decreased, then increased; 2 of the 14 trajectories stagnated at first, then began a decline. A comparison of observed weight and that estimated by modelling made it possible to correctly classify 97.6% of persons with "excess weight loss (EWL) >50% ", and more than 58% of persons with " EWL between 25 and 50% ". In the category of persons with "EWL >50% ", weight data over the first 6 months were adequate to correctly predict the observed result. Conclusions: this modelling allowed correct classification of persons with EWL >50%. Other studies are needed to validate this model in other populations, with other types of surgery and other medical-surgical teams.

  • CirrODS: a web-based clinical decision and workflow support tool for evidence-based management of patients with cirrhosis

    Date Submitted: Feb 7, 2019

    Open Peer Review Period: Feb 11, 2019 - Apr 8, 2019

    Background: There are gaps in delivering evidence-based care for patients with chronic liver disease and cirrhosis. Objective: Our objective was to use interactive user-centered design methods to deve...

    Background: There are gaps in delivering evidence-based care for patients with chronic liver disease and cirrhosis. Objective: Our objective was to use interactive user-centered design methods to develop the CirrODS (Cirrhosis Order set and clinical Decision Support) tool in order to improve clinical decision-making and workflow. Methods: Two workgroups were convened with clinicians, user-experience designers, human-factors and health-services researchers, and information technologists to create user-interface designs. CirrODS prototypes underwent several rounds of formative design. Physicians (n=20) at three hospitals were provided with clinical scenarios of patients with cirrhosis, and the admission orders made with and without the CirrODS tool were compared. The physicians rated their experience using CirrODS and provided comments, which we coded into categories and themes. We assessed the safety, usability, and quality of CirrODS using qualitative and quantitative methods. Results: We created an interactive CirrODS prototype that displays an alert when existing electronic data indicate a patient is at risk for cirrhosis. The tool consists of two primary frames, presenting relevant patient data and allowing recommended evidence-based tests and treatments to be ordered and categorized. Physicians viewed the tool positively and suggested that it would be most useful at the time of admission. When using the tool, the clinicians placed fewer orders than they placed when not using the tool, but more of the orders placed were considered to be “high priority” when the tool was used than when it was not used. The physicians’ ratings of CirrODS indicated above average usability. Conclusions: We developed a novel web-based combined clinical decision-making and workflow support tool to alert and assist clinicians caring for patients with cirrhosis. Further studies are underway to assess the impact on quality of care for patients with cirrhosis in actual practice.

  • Identification of Knee Osteoarthritis Based on Bayesian Network: A Pilot Study

    Date Submitted: Jan 30, 2019

    Open Peer Review Period: Feb 4, 2019 - Apr 1, 2019

    Background: Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations amongst existing classification or prediction...

    Background: Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations amongst existing classification or prediction models including invisible data processing and complicated dataset attributes, which hinder their applications in clinical practice. Objective: Develop a BN-based classification model to classify people with knee OA. The proposed model can be treated as a cheap and portable prescreening tool which can provide decision support for health professionals. Methods: A classification model is developed to classify knee OA based on Bayesian network (BN). The model’s structure is based on a three-level BN structure, and then retrained by the Bayesian Search (BS) learning algorithm. The model’s parameters are determined by the Expectation-Maximization (EM) algorithm. A total of 157 instances are adopted as the dataset which includes backgrounds (5 attributes, the basic characteristics of subjects), the target disease (namely the knee OA), and predictors (13 attributes, the scores of physical fitness tests). The performance of the model is evaluated based on classification accuracy, area under a curve (AUC), specificity and sensitivity, and is also compared with other well-known classification models. A test is also performed to explore whether physical fitness tests could improve the performance of the proposed model. Results: The proposed model’s results are higher than, or equal to, the mean scores of the other classification models: 0.754 for accuracy, 0.78 for AUC, 0.78 for specificity and 0.73 for sensitivity. Meanwhile, the proposed model also shows significant improvement when compared to the traditional BN model: 6.35% increase in accuracy (from 0.709 to 0.754), 4.00% increase in AUC (from 0.75 to 0.78), 6.85% increase in specificity (from 0.73 to 0.78) and 5.80% increase in sensitivity (from 0.69 to 0.73). Furthermore, the test results show that the performance of the proposed model could be largely enhanced through physical fitness tests in three evaluation indexes: 10.56% increase in accuracy (from 0.682 to 0.754), 16.42% increase in AUC (from 0.67 to 0.78) and 30.00% increase in specificity (from 0.60 to 0.78). Conclusions: The proposed model presents a promising method to classify people with knee OA when compared to other classification models and the traditional BN model. The proposed model could be implemented in clinical practice as a prescreening tool for knee OA, which could, not only improve the quality of healthcare for elderly people, but also reduce overall medical costs.

  • Core Data Elements in Acute Myeloid Leukemia

    Date Submitted: Jan 30, 2019

    Open Peer Review Period: Feb 4, 2019 - Apr 1, 2019

    Background: For cancer domains as Acute Myeloid Leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The...

    Background: For cancer domains as Acute Myeloid Leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses. Objective: To identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation based on a systematic metadata analysis of existing documentation models. Methods: Lists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System and then systematically analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format Operational Data Model by the Clinical Data Interchange Standards Consortium. Results: 3265 medical concepts were identified of which 1414 were unique. Among 1414 unique medical concepts, the 50 most frequent cover 27.0% percent of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.5% of all concepts occurrences. Implementation of common data elements is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems. Conclusions: Information management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.

  • Impact on Readmission Reduction Among Heart Failure Patients Using Digital Health Monitoring: Feasibility and Adoption in a Real World Setting

    Date Submitted: Jan 31, 2019

    Open Peer Review Period: Feb 1, 2019 - Mar 29, 2019

    Background: Congestive heart failure (CHF) is a condition that affects approximately 6.5 million people in the U.S. with a mortality rate of around 30%. With the incidence rate expected to rise by 46%...

    Background: Congestive heart failure (CHF) is a condition that affects approximately 6.5 million people in the U.S. with a mortality rate of around 30%. With the incidence rate expected to rise by 46% to exceed 8 million cases by 2030, projections estimate that total CHF costs will increase about to nearly $70 billion. Recently, the advent of remote monitoring technology has significantly broadened the scope of the physician’s reach in chronic disease management. Objective: The goal of this project was to see feasibility of using digital health monitoring in real world hospital setting, ascertain patient adoption and evaluate impact on 30-day readmission rate as primary outcome. Methods: A digital medicine software platform developed by Rx.Health, called RxUniverse, was used to prescribe HealthPROMISE and iHealth mobile apps to patients’ personal smartphones. Patients updated and recorded their CHF-related symptoms and quality of life measures daily on HealthPROMISE. Vital sign data, including blood pressure and weight, were collected through an ambulatory remote monitoring system that integrated the iHealth app and complementary consumer grade Bluetooth-connected smart devices (blood pressure cuff and digital scale). Physicians were notified of abnormal patient blood pressure and weight change readings and further action was left to the physician’s discretion. We used statistical analyses to determine risk factors associated with 30-day all-cause readmission. Results: Overall, the HeartHealth project included 60 patients admitted to Mount Sinai Hospital for CHF. There were six 30-day hospital readmissions (10% 30-day readmission rate), compared to the national readmission rates of around 25%. Single marital status (p = 0.064) and history of percutaneous coronary intervention (p = 0.075) were associated with readmission. Readmitted patients were also less likely to have been previously prescribed angiotensin converting enzyme inhibitors or angiotensin II receptor blockers (p = 0.019). Notably, readmitted patients utilized the blood pressure and weight monitors less than non-readmitted patients, and patients aged less than 70 used the monitors more frequently on average than those over 70, though these trends did not reach statistical significance. The percentage of patients using the monitors at least once dropped steadily from 83% in the first week after discharge to 46% in the fourth week. Additionally, 88% of patients used the monitor at least 4 times and 62% at least 10 times, with some patients using the monitors multiple times per day. Conclusions: Given the increasing burden of CHF, there is a need for an effective and sustainable remote monitoring system for CHF patients following hospital discharge. We identified clinical and social factors as well as remote monitoring usage trends that identify targetable patient populations that could benefit most from integration of daily remote monitoring. In addition, we demonstrated that interventions driven by real-time vitals data may greatly aid in reducing hospital readmissions and costs while improving patient outcomes. Future studies should seek to measure population health-wide impact by expanding digital health remote monitoring enterprise-wide.