JMIR Publications

JMIR Medical Informatics

Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.

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Journal Description

JMIR Medical Informatics (JMI, ISSN 2291-9694) 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 2016: 5.175), 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:

  • Source: Pixabay; Copyright: Free-Photos; URL: https://pixabay.com/en/hand-nail-pointing-fingers-screen-692113/; License: Public Domain (CC0).

    System-Wide Inpatient Portal
 Implementation: Survey of Health Care Team Perceptions

    Abstract:

    Background: Inpatient portals, a new type of patient portal tailored specifically to the hospital setting, can allow patients to access up-to-date health information and exchange secure communications with their care team. As such, inpatient portals present an opportunity for patients to increase engagement in their care during a time of acute crisis that emphasizes focus on a patient’s health. While there is a large body of research on patient portals in the outpatient setting, questions are being raised specifically about inpatient portals, such as how they will be incorporated into the flow of patient care in hectic, stressed, team-based hospital settings. Objective: Our aim is to improve understanding about hospital care team members’ perceptions of the value of an interactive patient portal for admitted patients, as well as to ascertain staff orientation toward this new technology. Methods: Throughout the course of 2016, an inpatient portal, MyChart Bedside (MCB) was implemented across a five-hospital health system. The portal is a tablet-based app that includes a daily schedule, lab/test results, secure messaging with the care team, a place to take notes, and access to educational materials. Within a month of initial rollout, hospital care team members completed a 5-minute, anonymous online survey to assess attitudes and perceptions about MCB use and staff training for the new technology. Results: Throughout the health system, 686 staff members completed the survey: 193 physicians (23.6%), 439 nurses (53.7%), and 186 support staff (22.7%). Questions about the importance of MCB, self-efficacy in using MCB with patients, and feelings about sufficient training and resources showed that an average of 40-60% of respondents in each group reported a positive orientation toward the MCB technology and training received. This positive orientation was highest among support staff, lower among nurses, and lowest for physicians (all differences by staff role were statistically significant at P<.001). Additionally, 62.0% of respondents reported “not enough” training. Conclusions: Despite the robust training effort, similar to that used in previous health information technology implementations at this health system, hospital care team members reported only a moderately positive orientation toward MCB and its potential, and the majority wanted more training. We propose that due to the unique elements of the inpatient portal—interactive features used by patients and providers requiring explanation and collaboration—traditional training approaches may be insufficient. Introduction of the inpatient portal as a new collaborative tool may thus require new methods of training to support enhanced engagement between patients and their care team.

  • Ophthalmology web form (montage). Source: The Authors / Placeit.net; Copyright: JMIR Publications; URL: http://medinform.jmir.org/2017/3/e27/; License: Creative Commons Attribution (CC-BY).

    Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application

    Abstract:

    Background: The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. Objective: The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Methods: Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. Results: The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. Conclusions: This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities.

  • Managing chronic conditions at home, with telemonitoring of vital signs and the administration of symptom questionnaires during trial. Source: Image created by the Authors with permission from trial participants; Copyright: CSIRO / The Authors; URL: http://medinform.jmir.org/2017/3/e29/; License: Creative Commons Attribution (CC-BY).

    Impact of At-Home Telemonitoring on Health Services Expenditure and Hospital Admissions in Patients With Chronic Conditions: Before and After Control...

    Abstract:

    Background: Telemonitoring is becoming increasingly important for the management of patients with chronic conditions, especially in countries with large distances such as Australia. However, despite large national investments in health information technology, little policy work has been undertaken in Australia in deploying telehealth in the home as a solution to the increasing demands and costs of managing chronic disease. Objective: The objective of this trial was to evaluate the impact of introducing at-home telemonitoring to patients living with chronic conditions on health care expenditure, number of admissions to hospital, and length of stay (LOS). Methods: A before and after control intervention analysis model was adopted whereby at each location patients were selected from a list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. Test patients were supplied with a telehealth vital signs monitor and were remotely managed by a trained clinical care coordinator, while control patients continued to receive usual care. A total of 100 test patients and 137 control patients were analyzed. Primary health care benefits provided to Australian patients were investigated for the trial cohort. Time series data were analyzed using linear regression and analysis of covariance for a period of 3 years before the intervention and 1 year after. Results: There were no significant differences between test and control patients at baseline. Test patients were monitored for an average of 276 days with 75% of patients monitored for more than 6 months. Test patients 1 year after the start of their intervention showed a 46.3% reduction in rate of predicted medical expenditure, a 25.5% reduction in the rate of predicted pharmaceutical expenditure, a 53.2% reduction in the rate of predicted unscheduled admission to hospital, a 67.9% reduction in the predicted rate of LOS when admitted to hospital, and a reduction in mortality of between 41.3% and 44.5% relative to control patients. Control patients did not demonstrate any significant change in their predicted trajectory for any of the above variables. Conclusions: At-home telemonitoring of chronically ill patients showed a statistically robust positive impact increasing over time on health care expenditure, number of admissions to hospital, and LOS as well as a reduction in mortality. Trial Registration: Retrospectively registered with the Australian and New Zealand Clinical Trial Registry ACTRN12613000635763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364030 (Archived by WebCite at http://www.webcitation.org/6sxqjkJHW)

  • Adapted from the sociotechnical model [43]. Source: Figure 2 from http://medinform.jmir.org/2017/3/e28; Copyright: the authors; License: Creative Commons Attribution (CC-BY).

    Health Information Technology (HIT) Adaptation: Refocusing on the Journey to Successful HIT Implementation

    Abstract:

    In past years, policies and regulations required hospitals to implement advanced capabilities of certified electronic health records (EHRs) in order to receive financial incentives. This has led to accelerated implementation of health information technologies (HIT) in health care settings. However, measures commonly used to evaluate the success of HIT implementation, such as HIT adoption, technology acceptance, and clinical quality, fail to account for complex sociotechnical variability across contexts and the different trajectories within organizations because of different implementation plans and timelines. We propose a new focus, HIT adaptation, to illuminate factors that facilitate or hinder the connection between use of the EHR and improved quality of care as well as to explore the trajectory of changes in the HIT implementation journey as it is impacted by frequent system upgrades and optimizations. Future research should develop instruments to evaluate the progress of HIT adaptation in both its longitudinal design and its focus on adaptation progress rather than on one cross-sectional outcome, allowing for more generalizability and knowledge transfer.

  • Source: Wikimedia Commons; Copyright: Calleamanecer; URL: https://commons.wikimedia.org/wiki/File:Clinicians_in_Intensive_Care_Unit.jpg; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Is There Evidence of Cost Benefits of Electronic Medical Records, Standards, or Interoperability in Hospital Information Systems? Overview of Systematic Reviews

    Abstract:

    Background: Electronic health (eHealth) interventions may improve the quality of care by providing timely, accessible information about one patient or an entire population. Electronic patient care information forms the nucleus of computerized health information systems. However, interoperability among systems depends on the adoption of information standards. Additionally, investing in technology systems requires cost-effectiveness studies to ensure the sustainability of processes for stakeholders. Objective: The objective of this study was to assess cost-effectiveness of the use of electronically available inpatient data systems, health information exchange, or standards to support interoperability among systems. Methods: An overview of systematic reviews was conducted, assessing the MEDLINE, Cochrane Library, LILACS, and IEEE Library databases to identify relevant studies published through February 2016. The search was supplemented by citations from the selected papers. The primary outcome sought the cost-effectiveness, and the secondary outcome was the impact on quality of care. Independent reviewers selected studies, and disagreement was resolved by consensus. The quality of the included studies was evaluated using a measurement tool to assess systematic reviews (AMSTAR). Results: The primary search identified 286 papers, and two papers were manually included. A total of 211 were systematic reviews. From the 20 studies that were selected after screening the title and abstract, 14 were deemed ineligible, and six met the inclusion criteria. The interventions did not show a measurable effect on cost-effectiveness. Despite the limited number of studies, the heterogeneity of electronic systems reported, and the types of intervention in hospital routines, it was possible to identify some preliminary benefits in quality of care. Hospital information systems, along with information sharing, had the potential to improve clinical practice by reducing staff errors or incidents, improving automated harm detection, monitoring infections more effectively, and enhancing the continuity of care during physician handoffs. Conclusions: This review identified some benefits in the quality of care but did not provide evidence that the implementation of eHealth interventions had a measurable impact on cost-effectiveness in hospital settings. However, further evidence is needed to infer the impact of standards adoption or interoperability in cost benefits of health care; this in turn requires further research.

  • iCMP program (montage). Source: FreeDigitalPhotos.net; Copyright: The Authors; URL: http://medinform.jmir.org/2017/3/e25/; License: Licensed by the authors.

    Optimizing the Use of Electronic Health Records to Identify High-Risk Psychosocial Determinants of Health

    Abstract:

    Background: Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data. However, not all clinically relevant data reside in claims and formatted data. Recently, there has been increasing interest in including patients with complex psychosocial determinants of health in care coordination programs. Psychosocial risk factors, including social determinants of health, mental health disorders, and substance abuse disorders, are less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data, and due to US Health Insurance Portability and Accountability Act (HIPAA) regulations are often not available as claims data. Objective: The objective of our study was to develop a systematic approach using word recognition software to identifying psychosocial risk factors within any part of a patient’s electronic health record (EHR). Methods: We used QPID (Queriable Patient Inference Dossier), an ontology-driven word recognition software, to scan adult patients’ EHRs to identify terms predicting a high-risk patient suitable to be followed in a care coordination program in Massachusetts, USA. Search terms identified high-risk conditions in patients known to be enrolled in a care coordination program, and were then tested against control patients. We calculated precision, recall, and balanced F-measure for the search terms. Results: We identified 22 EHR-available search terms to define psychosocial high-risk status; the presence of 9 or more of these terms predicted that a patient would meet inclusion criteria for a care coordination program. Precision was .80, recall .98, and balanced F-measure .88 for the identified terms. For adult patients insured by Medicaid and enrolled in the program, a mean of 14 terms (interquartile range [IQR] 11-18) were present as identified by the search tool, ranging from 2 to 22 terms. For patients enrolled in the program but not insured by Medicaid, a mean of 6 terms (IQR 3-8) were present as identified by the search tool, ranging from 1 to 21. Conclusions: Selected informatics tools such as word recognition software can be leveraged to improve health care delivery, such as an EHR-based protocol that identifies psychosocially complex patients eligible for enrollment in a care coordination program.

  • Source: Pixabay; Copyright: Wynn Pointaux; URL: https://pixabay.com/en/big-data-data-statistics-analytics-2296821/; License: Public Domain (CC0).

    Hierarchical Medical System Based on Big Data and Mobile Internet: A New Strategic Choice in Health Care

    Abstract:

    China is setting up a hierarchical medical system to solve the problems of biased resource allocation and high patient flows to large hospitals. The development of big data and mobile Internet technology provides a new perspective for the establishment of hierarchical medical system. This viewpoint discusses the challenges with the hierarchical medical system in China and how big data and mobile Internet can be used to mitigate these challenges.

  • Source: Image created by the authors; Copyright: The authors; URL: http://medinform.jmir.org/2017/3/e24/; License: Creative Commons Attribution (CC-BY).

    Clinical Note Creation, Binning, and Artificial Intelligence

    Abstract:

    The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans.

  • Source: Pixabay; Copyright: Gerd Altmann; URL: https://pixabay.com/fr/l-homme-gar%C3%A7on-%C3%A9l%C3%A9gant-internet-475557/; License: Public Domain (CC0).

    What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

    Abstract:

    Background: Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer. Objective: Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]). Methods: First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life. Results: Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients’ concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus. Conclusions: We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life.

  • Patient advocate recording patient complaint. Source: FreeDigitalPhotos.net; Copyright: Ambro; URL: http://www.freedigitalphotos.net/images/Healthcare_g355-Psychiatrist_With_Patient_p36553.html; License: Licensed by the authors.

    Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers

    Abstract:

    Background: Unsolicited patient complaints can be a useful service recovery tool for health care organizations. Some patient complaints contain information that may necessitate further action on the part of the health care organization and/or the health care professional. Current approaches depend on the manual processing of patient complaints, which can be costly, slow, and challenging in terms of scalability. Objective: The aim of this study was to evaluate automatic patient triage, which can potentially improve response time and provide much-needed scale, thereby enhancing opportunities to encourage physicians to self-regulate. Methods: We implemented a comparison of several well-known machine learning classifiers to detect whether a complaint was associated with a physician or his/her medical practice. We compared these classifiers using a real-life dataset containing 14,335 patient complaints associated with 768 physicians that was extracted from patient complaints collected by the Patient Advocacy Reporting System developed at Vanderbilt University and associated institutions. We conducted a 10-splits Monte Carlo cross-validation to validate our results. Results: We achieved an accuracy of 82% and F-score of 81% in correctly classifying patient complaints with sensitivity and specificity of 0.76 and 0.87, respectively. Conclusions: We demonstrate that natural language processing methods based on modeling patient complaint text can be effective in identifying those patient complaints requiring physician action.

  • Source: FreeDigitalPhotos.net; Copyright: Anusorn P nachol; URL: http://www.freedigitalphotos.net/images/human-kidney-in-hand-photo-p175675; License: Licensed by the authors.

    Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the...

    Abstract:

    Background: Chronic kidney disease (CKD) is a major public health concern in the United States with high prevalence, growing incidence, and serious adverse outcomes. Objective: We aimed to develop and validate a model to identify patients at risk of receiving a new diagnosis of CKD (incident CKD) during the next 1 year in a general population. Methods: The study population consisted of patients who had visited any care facility in the Maine Health Information Exchange network any time between January 1, 2013, and December 31, 2015, and had no history of CKD diagnosis. Two retrospective cohorts of electronic medical records (EMRs) were constructed for model derivation (N=1,310,363) and validation (N=1,430,772). The model was derived using a gradient tree-based boost algorithm to assign a score to each individual that measured the probability of receiving a new diagnosis of CKD from January 1, 2014, to December 31, 2014, based on the preceding 1-year clinical profile. A feature selection process was conducted to reduce the dimension of the data from 14,680 EMR features to 146 as predictors in the final model. Relative risk was calculated by the model to gauge the risk ratio of the individual to population mean of receiving a CKD diagnosis in next 1 year. The model was tested on the validation cohort to predict risk of CKD diagnosis in the period from January 1, 2015, to December 31, 2015, using the preceding 1-year clinical profile. Results: The final model had a c-statistic of 0.871 in the validation cohort. It stratified patients into low-risk (score 0-0.005), intermediate-risk (score 0.005-0.05), and high-risk (score ≥ 0.05) levels. The incidence of CKD in the high-risk patient group was 7.94%, 13.7 times higher than the incidence in the overall cohort (0.58%). Survival analysis showed that patients in the 3 risk categories had significantly different CKD outcomes as a function of time (P<.001), indicating an effective classification of patients by the model. Conclusions: We developed and validated a model that is able to identify patients at high risk of having CKD in the next 1 year by statistically learning from the EMR-based clinical history in the preceding 1 year. Identification of these patients indicates care opportunities such as monitoring and adopting intervention plans that may benefit the quality of care and outcomes in the long term.

  • Source: Pexels.com; Copyright: Startup Stock Photos; URL: https://www.pexels.com/photo/working-woman-technology-computer-7374/; License: Public Domain (CC0).

    DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy

    Abstract:

    Background: In recent years, the assessment of mental disorders has become more and more personalized. Modern advancements such as Internet-enabled mobile phones and increased computing capacity make it possible to tap sources of information that have long been unavailable to mental health practitioners. Objective: Software packages that combine algorithm-based treatment planning, process monitoring, and outcome monitoring are scarce. The objective of this study was to assess whether the DynAMo Web application can fill this gap by providing a software solution that can be used by both researchers to conduct state-of-the-art psychotherapy process research and clinicians to plan treatments and monitor psychotherapeutic processes. Methods: In this paper, we report on the current state of a Web application that can be used for assessing the temporal structure of mental disorders using information on their temporal and synchronous associations. A treatment planning algorithm automatically interprets the data and delivers priority scores of symptoms to practitioners. The application is also capable of monitoring psychotherapeutic processes during therapy and of monitoring treatment outcomes. This application was developed using the R programming language (R Core Team, Vienna) and the Shiny Web application framework (RStudio, Inc, Boston). It is made entirely from open-source software packages and thus is easily extensible. Results: The capabilities of the proposed application are demonstrated. Case illustrations are provided to exemplify its usefulness in clinical practice. Conclusions: With the broad availability of Internet-enabled mobile phones and similar devices, collecting data on psychopathology and psychotherapeutic processes has become easier than ever. The proposed application is a valuable tool for capturing, processing, and visualizing these data. The combination of dynamic assessment and process- and outcome monitoring has the potential to improve the efficacy and effectiveness of psychotherapy.

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  • Understanding barriers to physicians adopting clinical decision support systems: A Systematic Review

    Date Submitted: Sep 6, 2017

    Open Peer Review Period: Sep 8, 2017 - Nov 3, 2017

    Background: The purpose of this paper was to conduct an analytical review and task analysis of CDSS re-search, and to develop a new framework for CDSS design in order to achieve user acceptance. Objec...

    Background: The purpose of this paper was to conduct an analytical review and task analysis of CDSS re-search, and to develop a new framework for CDSS design in order to achieve user acceptance. Objective: This paper tackles a critical issue modern healthcare systems namely, the adoption of Health Informatics Technologies (HIT). The paper provides a comprehensive literature review of the current status quo of Clinical Decision Support Systems adoption, the barriers to adoption from users stand point, and proposes novel solutions to current gaps in order to the enhancement of healthcare delivery and execution. Methods: A literature review of CDSS papers was conducted with a focus on user acceptance. In order to gain a greater understanding of the problems associated with CDSS, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives. Results: Based on the literature review, findings are characterized by whether user acceptance was favorable and unfavorable for clinical guidelines, reminders, and diagnostic CDSS. We propose two models: 1) the User Acceptance and System Adaptation Design (UASAD) model which includes optimizing CDSS design based on user needs/expectations and 2) the Input-Process-Output-Engage (IPOE) model which reveals to users the processes the govern CDSS outputs. Conclusions: CDSS have the capacity to decrease medical errors and improve patient outcomes. However, current CDSS designs do not consistently show such improvements. We hypothesized that the incorporation of the proposed models will improve user acceptance to support beneficial impacts of CDSS adoption. Ultimately, if a user does not accept technology this not only poses a threat to the use of the technology but can also pose a threat to the health and wellbeing of patients.

  • Healthcare Information Systems and Technology: A Systematic Review

    Date Submitted: Jul 31, 2017

    Open Peer Review Period: Jul 31, 2017 - Sep 25, 2017

    The present article offers a systematic mapping of studies related to information systems and technology in the field of health care. Articles were selected and compiled from prestigious publications...

    The present article offers a systematic mapping of studies related to information systems and technology in the field of health care. Articles were selected and compiled from prestigious publications that combine knowledge related to information systems, science and health. This facilitates establishing a distribution of the available literature, obtaining as a result a map of knowledge that structures the different trends and main areas of research in this area. Finally, we project and propose future research for those trends that stand out because of their interest and the possibility of deepening them.

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