JMIR Publications

JMIR Medical Informatics

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


Journal Description

JMIR Medical Informatics (JMI, ISSN 2291-9694) focusses 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 2014: 3.4), JMIR Med Inform has a 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 (


Recent Articles:

  • Source:, CC0 Licensed, Public Domain; modified by authors.

    A Legal Framework to Support Development and Assessment of Digital Health Services


    Background: Digital health services empower people to track, manage, and improve their own health and quality of life while delivering a more personalized and precise health care, at a lower cost and with higher efficiency and availability. Essential for the use of digital health services is that the treatment of any personal data is compatible with the Patient Data Act, Personal Data Act, and other applicable privacy laws. Objective: The aim of this study was to develop a framework for legal challenges to support designers in development and assessment of digital health services. Methods: A purposive sampling, together with snowball recruitment, was used to identify stakeholders and information sources for organizing, extending, and prioritizing the different concepts, actors, and regulations in relation to digital health and health-promoting digital systems. The data were collected through structured interviewing and iteration, and 3 different cases were used for face validation of the framework. Results: A framework for assessing the legal challenges in developing digital health services (Legal Challenges in Digital Health [LCDH] Framework) was created and consists of 6 key questions to be used to evaluate a digital health service according to current legislation. Conclusions: Structured discussion about legal challenges in relation to health-promoting digital services can be enabled by a constructive framework to investigate, assess, and verify the digital service according to current legislation. The LCDH Framework developed in this study proposes such a framework and can be used in prospective evaluation of the relationship of a potential health-promoting digital service with the existing laws and regulations

  • Author working with colleagues. Source and copyright: the author TM.

    Putting Meaning into Meaningful Use: A Roadmap to Successful Integration of Evidence at the Point of Care

    Authors List:


    Pressures to contain health care costs, personalize patient care, use big data, and to enhance health care quality have highlighted the need for integration of evidence at the point of care. The application of evidence-based medicine (EBM) has great promise in the era of electronic health records (EHRs) and health technology. The most successful integration of evidence into EHRs has been complex decision tools that trigger at a critical point of the clinical visit and include patient specific recommendations. The objective of this viewpoint paper is to investigate why the incorporation of complex CDS tools into the EMR is equally complex and continues to challenge health service researchers and implementation scientists. Poor adoption and sustainability of EBM guidelines and CDS tools at the point of care have persisted and continue to document low rates of usage. The barriers cited by physicians include efficiency, perception of usefulness, information content, user interface, and over-triggering. Building on the traditional EHR implementation frameworks, we review keys strategies for successful CDSs: (1) the quality of the evidence, (2) the potential to reduce unnecessary care, (3) ease of integrating evidence at the point of care, (4) the evidence’s consistency with clinician perceptions and preferences, (5) incorporating bundled sets or automated documentation, and (6) shared decision making tools. As EHRs become commonplace and insurers demand higher quality and evidence-based care, better methods for integrating evidence into everyday care are warranted. We have outlined basic criteria that should be considered before attempting to integrate evidenced-based decision support tools into the EHR.

  • Source:; CC0 1.0 Public Domain.

    Impact of Implementing a Wiki to Develop Structured Electronic Order Sets on Physicians' Intention to Use Wiki-Based Order Sets


    Background: Wikis have the potential to promote best practices in health systems by sharing order sets with a broad community of stakeholders. However, little is known about the impact of using a wiki on clinicians’ intention to use wiki-based order sets. Objective: The aims of this study were: (1) to describe the use of a wiki to create structured order sets for a single emergency department; (2) to evaluate whether the use of this wiki changed emergency physicians’ future intention to use wiki-based order sets; and (3) to understand the impact of using the wiki on the behavioral determinants for using wiki-based order sets. Methods: This was a pre/post-intervention mixed-methods study conducted in one hospital in Lévis, Quebec. The intervention was comprised of receiving access to and being motivated by the department head to use a wiki for 6 months to create electronic order sets designed to be used in a computer physician order entry system. Before and after our intervention, we asked participants to complete a previously validated questionnaire based on the Theory of Planned Behavior. Our primary outcome was the intention to use wiki-based order sets in clinical practice. We also assessed participants’ attitude, perceived behavioral control, and subjective norm to use wiki-based order sets. Paired pre- and post-Likert scores were compared using Wilcoxon signed-rank tests. The post-questionnaire also included open-ended questions concerning participants’ comments about the wiki, which were then classified into themes using an existing taxonomy. Results: Twenty-eight emergency physicians were enrolled in the study (response rate: 100%). Physicians’ mean intention to use a wiki-based reminder was 5.42 (SD 1.04) before the intervention, and increased to 5.81 (SD 1.25) on a 7-point Likert scale (P=.03) after the intervention. Participants’ attitude towards using a wiki-based order set also increased from 5.07 (SD 0.90) to 5.57 (SD 0.88) (P=.003). Perceived behavioral control and subjective norm did not change. Easier information sharing was the most frequently positive impact raised. In order of frequency, the three most important facilitators reported were: ease of use, support from colleagues, and promotion by the departmental head. Although participants did not mention any perceived negative impacts, they raised the following barriers in order of frequency: poor organization of information, slow computers, and difficult wiki access. Conclusions: Emergency physicians’ intention and attitude to use wiki-based order sets increased after having access to and being motivated to use a wiki for 6 months. Future studies need to explore if this increased intention will translate into sustained actual use and improve patient care. Certain barriers need to be addressed before implementing a wiki for use on a larger scale.

  • A Clinician and EHR. Source and copyright: the author.

    Electronic Health Record-Related Safety Concerns: A Cross-Sectional Survey of Electronic Health Record Users


    Background: The rapid expansion in the use of electronic health records (EHR) has increased the number of medical errors originating in health information systems (HIS). The sociotechnical approach helps in understanding risks in the development, implementation, and use of EHR and health information technology (HIT) while accounting for complex interactions of technology within the health care system. Objective: This study addresses two important questions: (1) “which of the common EHR error types are associated with perceived high- and extreme-risk severity ratings among EHR users?”, and (2) “which variables are associated with high- and extreme-risk severity ratings?” Methods: This study was a quantitative, non-experimental, descriptive study of EHR users. We conducted a cross-sectional web-based questionnaire study at the largest hospital district in Finland. Statistical tests included the reliability of the summative scales tested with Cronbach’s alpha. Logistic regression served to assess the association of the independent variables to each of the eight risk factors examined. Results: A total of 2864 eligible respondents provided the final data. Almost half of the respondents reported a high level of risk related to the error type “extended EHR unavailability”. The lowest overall risk level was associated with “selecting incorrectly from a list of items”. In multivariate analyses, profession and clinical unit proved to be the strongest predictors for high perceived risk. Physicians perceived risk levels to be the highest (P <.001 in six of eight error types), while emergency departments, operating rooms, and procedure units were associated with higher perceived risk levels (P <.001 in four of eight error types). Previous participation in eLearning courses on EHR-use was associated with lower risk for some of the risk factors. Conclusions: Based on a large number of Finnish EHR users in hospitals, this study indicates that HIT safety hazards should be taken very seriously, particularly in operating rooms, procedure units, emergency departments, and intensive care units/critical care units. Health care organizations should use proactive and systematic assessments of EHR risks before harmful events occur. An EHR training program should be compulsory for all EHR users in order to address EHR safety concerns resulting from the failure to use HIT appropriately.

  • Using a Phoropter. Source: CC 2.0 Licensed, Attribution: National Eye Institute.

    Creation of an Accurate Algorithm to Detect Snellen Best Documented Visual Acuity from Ophthalmology Electronic Health Record Notes


    Background: Visual acuity is the primary measure used in ophthalmology to determine how well a patient can see. Visual acuity for a single eye may be recorded in multiple ways for a single patient visit (eg, Snellen vs. Jäger units vs. font print size), and be recorded for either distance or near vision. Capturing the best documented visual acuity (BDVA) of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research. Objective: Currently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record (EHR) notes. We developed an algorithm to detect BDVA for right and left eyes from defined fields within electronic ophthalmology clinical notes. Methods: We designed an algorithm to detect the BDVA from defined fields within 295,218 ophthalmology clinical notes with visual acuity data present. About 5668 unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities. Results: Visual acuity was captured from a total of 295,218 ophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye. A clinician chart review of 100 random patient notes showed a 99% accuracy detecting BDVA from these records and 1% observed error. Conclusions: Our algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of our knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes. Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. All codes used for this study are currently available, and will be made available online at

  • Photo by hin255. Published on 01 April 2013 Stock photo - Image ID: 100152858;

    Use and Uptake of eHealth in General Practice: A Cross-Sectional Survey and Focus Group Study Among Health Care Users and General Practitioners


    Background: Policy makers promote the use of eHealth to widen access to health care services and to improve the quality and safety of care. Nevertheless, the enthusiasm among policy makers for eHealth does not match its uptake and use. eHealth is defined in this study as “health services delivered or enhanced through the Internet and related information and communication technologies.” Objective: The objective of this study was to investigate (1) the current use of eHealth in the Netherlands by general practitioners (GPs) and health care users, (2) the future plans of GPs to provide eHealth and the willingness of health care users to use eHealth services, and (3) the perceived positive effects and barriers from the perspective of GPs and health care users. Methods: A cross-sectional survey of a sample of Dutch GPs and members of the Dutch Health Care Consumer Panel was conducted in April 2014. A pre-structured questionnaire was completed by 171 GPs (12% response) and by 754 health care users (50% response). In addition, two focus groups were conducted in June 2014: one group with GPs (8 participants) and one with health care users (10 participants). Results: Three-quarters of Dutch GPs that responded to the questionnaire (67.3%, 115/171) offered patients the possibility of requesting a prescription via the Internet, and half of them offered patients the possibility of asking a question via the Internet (49.1%, 84/171). In general, they did intend to provide future eHealth services. Nonetheless, many of the GPs perceived barriers, especially concerning its innovation (eg, insufficient reliable, secure systems) and the sociopolitical context (eg, lack of financial compensation for the time spent on implementation). By contrast, health care users were generally not aware of existing eHealth services offered by their GPs. Nevertheless, half of them were willing to use eHealth services when offered by their GP. In general, health care users have positive attitudes regarding eHealth. One in five (20.6%, 148/718) health care users perceived barriers to the use of eHealth. These included concerns about the safety of health information obtained via the Internet (66.7%, 96/144) and privacy aspects (55.6%, 80/144). Conclusions: GPs and health care users have generally positive attitudes towards eHealth, which is a prerequisite for the uptake of eHealth. But, general practitioners in particular perceive barriers to using eHealth and consider the implementation of eHealth to be complex. This study shows that there is room for improving awareness of eHealth services in primary care. It will take some time before these issues are resolved and eHealth can be fully adopted.

  • Schematic illustration of the study design, created and uploaded by the authors.

    A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources


    Background: Health level seven version 2.5 (HL7 v2.5) is a widespread messaging standard for information exchange between clinical information systems. By applying Semantic Web technologies for handling HL7 v2.5 messages, it is possible to integrate large-scale clinical data with life science knowledge resources. Objective: Showing feasibility of a querying method over large-scale resource description framework (RDF)-ized HL7 v2.5 messages using publicly available drug databases. Methods: We developed a method to convert HL7 v2.5 messages into the RDF. We also converted five kinds of drug databases into RDF and provided explicit links between the corresponding items among them. With those linked drug data, we then developed a method for query expansion to search the clinical data using semantic information on drug classes along with four types of temporal patterns. For evaluation purpose, medication orders and laboratory test results for a 3-year period at the University of Tokyo Hospital were used, and the query execution times were measured. Results: Approximately 650 million RDF triples for medication orders and 790 million RDF triples for laboratory test results were converted. Taking three types of query in use cases for detecting adverse events of drugs as an example, we confirmed these queries were represented in SPARQL Protocol and RDF Query Language (SPARQL) using our methods and comparison with conventional query expressions were performed. The measurement results confirm that the query time is feasible and increases logarithmically or linearly with the amount of data and without diverging. Conclusions: The proposed methods enabled query expressions that separate knowledge resources and clinical data, thereby suggesting the feasibility for improving the usability of clinical data by enhancing the knowledge resources. We also demonstrate that when HL7 v2.5 messages are automatically converted into RDF, searches are still possible through SPARQL without modifying the structure. As such, the proposed method benefits not only our hospitals, but also numerous hospitals that handle HL7 v2.5 messages. Our approach highlights a potential of large-scale data federation techniques to retrieve clinical information, which could be applied as applications of clinical intelligence to improve clinical practices, such as adverse drug event monitoring and cohort selection for a clinical study as well as discovering new knowledge from clinical information.

  • Doctors reviewing charts on tablet. iStock ID26076669. Copyright: Burlingham, license purchased by authors. 

    Understanding the Impact of Electronic Medical Record Use on Practice-Based Population Health Management: A Mixed-Method Study


    Background: Practice-based population health (PBPH) management is the proactive management of patients by their primary care clinical team. The ability of clinics to engage in PBPH and the means by which they incorporate it in a clinical setting remain unknown. Objective: We conducted the Canadian Population Health Management Challenge to determine the capacity and preparedness of primary care settings to engage in PBPH using their existing medical record systems and to understand the complexities that may exist in PBPH implementation. Methods: We recruited a sample of electronic medical record (EMR) -enabled and paper-based clinics from across Canada to participate in the challenge. The challenge required clinic staff and physicians to complete time-controlled, evidence-based practice reviews of their patients who may benefit from evidence-informed care, treatment, or interventions across five different areas (immunization, postmyocardial infarction care, cancer screening, diabetes management, and medication recall). We formulated a preparedness index to measure the capacity of clinics to engage in PBPH management. Finally, we conducted follow-up qualitative interviews to provide richer understanding of PBPH implementation and related issues (ie, challenges and facilitators). Results: A total of 11 primary care clinics participated, representing 21 clinician practices. EMR-enabled clinics completed a full review of charts in an average of 1.37 hours. On the contrary, paper-based clinics reviewed nearly 10% of their charts in an average of 3.9 hours, hinting that they would have required an estimated 40 hours to complete a review of charts in their practice. Furthermore, the index revealed a major gap in preparedness between the EMR and paper-based clinics (0.86–3.78 vs 0.05–0.12), as well as a broad range among the EMR clinics. Finally, building on the results of the qualitative analysis, we identified factors facilitating the integration of PBPH. Conclusions: Our results suggest that EMR usage is pivotal in setting the foundation to support PBPH. The wide range of performance variation among EMR-enabled clinics suggests that EMR functionality and optimization, its support of clinical practice workflow, and policy issues to ensure adoption of standards are critical issues to facilitate PBPH.

  • Source:
Licensed under CC BY-SA 2.0.

    Health Information Technology: Meaningful Use and Next Steps to Improving Electronic Facilitation of Medication Adherence


    Background: The use of health information technology (HIT) may improve medication adherence, but challenges for implementation remain. Objective: The aim of this paper is to review the current state of HIT as it relates to medication adherence programs, acknowledge the potential barriers in light of current legislation, and provide recommendations to improve ongoing medication adherence strategies through the use of HIT. Methods: We describe four potential HIT barriers that may impact interoperability and subsequent medication adherence. Legislation in the United States has incentivized the use of HIT to facilitate and enhance medication adherence. The Health Information Technology for Economic and Clinical Health (HITECH) was recently adopted and establishes federal standards for the so-called "meaningful use" of certified electronic health record (EHR) technology that can directly impact medication adherence. Results: The four persistent HIT barriers to medication adherence include (1) underdevelopment of data reciprocity across clinical, community, and home settings, limiting the capture of data necessary for clinical care; (2) inconsistent data definitions and lack of harmonization of patient-focused data standards, making existing data difficult to use for patient-centered outcomes research; (3) inability to effectively use the national drug code information from the various electronic health record and claims datasets for adherence purposes; and (4) lack of data capture for medication management interventions, such as medication management therapy (MTM) in the EHR. Potential recommendations to address these issues are discussed. Conclusion: To make meaningful, high quality data accessible, and subsequently improve medication adherence, these challenges will need to be addressed to fully reach the potential of HIT in impacting one of our largest public health issues.

  • Snapshot of computerized adaptive testing (CAT)-based inpatient perception questionnaire for patients.
From: Chien TW, Wang WC, Huang SY, Lai WP, Chow JC. A Web-Based Computerized Adaptive Testing (CAT) to Assess Patient Perception in Hospitalization. J Med Internet Res 2011;13(3):e61.
File URL:
License: CC-BY-2.0.

    Improving Inpatient Surveys: Web-Based Computer Adaptive Testing Accessed via Mobile Phone QR Codes


    Background: The National Health Service (NHS) 70-item inpatient questionnaire surveys inpatients on their perceptions of their hospitalization experience. However, it imposes more burden on the patient than other similar surveys. The literature shows that computerized adaptive testing (CAT) based on item response theory can help shorten the item length of a questionnaire without compromising its precision. Objective: Our aim was to investigate whether CAT can be (1) efficient with item reduction and (2) used with quick response (QR) codes scanned by mobile phones. Methods: After downloading the 2008 inpatient survey data from the Picker Institute Europe website and analyzing the difficulties of this 70-item questionnaire, we used an author-made Excel program using the Rasch partial credit model to simulate 1000 patients’ true scores followed by a standard normal distribution. The CAT was compared to two other scenarios of answering all items (AAI) and the randomized selection method (RSM), as we investigated item length (efficiency) and measurement accuracy. The author-made Web-based CAT program for gathering patient feedback was effectively accessed from mobile phones by scanning the QR code. Results: We found that the CAT can be more efficient for patients answering questions (ie, fewer items to respond to) than either AAI or RSM without compromising its measurement accuracy. A Web-based CAT inpatient survey accessed by scanning a QR code on a mobile phone was viable for gathering inpatient satisfaction responses. Conclusions: With advances in technology, patients can now be offered alternatives for providing feedback about hospitalization satisfaction. This Web-based CAT is a possible option in health care settings for reducing the number of survey items, as well as offering an innovative QR code access.

  • Image Source: John Maddin via FlickR, CC BY-ND 2.0.

    Conceptual Models in Health Informatics Research: A Literature Review and Suggestions for Development


    Background: Contributing to health informatics research means using conceptual models that are integrative and explain the research in terms of the two broad domains of health science and information science. However, it can be hard for novice health informatics researchers to find exemplars and guidelines in working with integrative conceptual models. Objectives: The aim of this paper is to support the use of integrative conceptual models in research on information and communication technologies in the health sector, and to encourage discussion of these conceptual models in scholarly forums. Methods: A two-part method was used to summarize and structure ideas about how to work effectively with conceptual models in health informatics research that included (1) a selective review and summary of the literature of conceptual models; and (2) the construction of a step-by-step approach to developing a conceptual model. Results: The seven-step methodology for developing conceptual models in health informatics research explained in this paper involves (1) acknowledging the limitations of health science and information science conceptual models; (2) giving a rationale for one’s choice of integrative conceptual model; (3) explicating a conceptual model verbally and graphically; (4) seeking feedback about the conceptual model from stakeholders in both the health science and information science domains; (5) aligning a conceptual model with an appropriate research plan; (6) adapting a conceptual model in response to new knowledge over time; and (7) disseminating conceptual models in scholarly and scientific forums. Conclusions: Making explicit the conceptual model that underpins a health informatics research project can contribute to increasing the number of well-formed and strongly grounded health informatics research projects. This explication has distinct benefits for researchers in training, research teams, and researchers and practitioners in information, health, and other disciplines.

  • (cc) Cuba Gyllensten et al.
Credit License: CC-BY-SA 2.0.

    Early Indication of Decompensated Heart Failure in Patients on Home-Telemonitoring: A Comparison of Prediction Algorithms Based on Daily Weight and...


    Background: Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation. Objective: In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations. Methods: We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM). Results: Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines. Conclusions: NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation.

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  • The State of Open Source Electronic Health Record Projects: A Software Anthropology Study

    Date Submitted: Apr 6, 2016

    Open Peer Review Period: Apr 7, 2016 - Jun 2, 2016

    Background: Electronic health records are key tools in managing and storing patients’ illness information. Several open source EHR applications are available at SourceForge, the largest repository o...

    Background: Electronic health records are key tools in managing and storing patients’ illness information. Several open source EHR applications are available at SourceForge, the largest repository of open source projects. However, few published studies that focused on the characteristics of F/OSS electronic health record software and none to date has been published on the motivation, knowledge background, and demographic characteristics of the developers involved in open source electronic health records (EHR) projects. Functionality and usability are important success factors for any system. These are often a direct reflection of the domain knowledge and motivations of the developers. This is particularly true in open source development, where it is often said the genesis of many open source projects is “scratching one’s own itch”. Objective: This study analyzes the characteristics of F/OSS electronic health record software and aims to provide an understanding of the motivation, knowledge background, and characteristics of the developers involved in open source electronic health records (EHR) projects. Methods: The research identified F/OSS electronic health record projects on SourceForge and other websites starting in May 2014. Projects were classified and characterized by :license type , downloads, programming languages, spoken languages, project age, development status, supporting materials, top downloads according to countries, and certified open EHR. Healthcare F/OSS developers were also surveyed using an online survey website (SurveyGizmo). Results: There are 54 open source EHR projects, but only four of them are CMS certified applications. The majority of the projects, 44.5%, have been downloaded by users in the United States, which reveals a significant interest in EHR open source applications in the U.S. A total of 103 responses were successfully collected showing the reasons to contribute to a healthcare open source project. The top motivation factor (56.5%) is intrinsic enjoyment. The majority of users are paid for participation in F/OSS projects. Only twenty-five percent of healthcare F/OSS developers are, of have been, healthcare providers in their careers, and 45.9% of developers do not work in the healthcare field. Conclusions: Open source software seems to be important to many healthcare organizations in general; however, given that only four F/OSS EHR systems are U.S. ONC certified, this highlights a challenge for the U.S. adoption of F/OSS healthcare systems. Healthcare open source software currently lacks the governmental support for sustainability and growth of these software programs. This research highlights some challenges that may be hindering the future of healthcare F/OSS.