The State of Open Source Electronic Health Record Projects: A Software Anthropology Study
Bibliographic record
Abstract
BACKGROUND: Electronic health records (EHR) are a key tool in managing and storing patients' information. Currently, there are over 50 open source EHR systems available. Functionality and usability are important factors for determining the success of any system. These factors are often a direct reflection of the domain knowledge and developers' motivations. However, few published studies have focused on the characteristics of free and open source software (F/OSS) EHR systems and none to date have discussed the motivation, knowledge background, and demographic characteristics of the developers involved in open source EHR projects. OBJECTIVE: This study analyzed the characteristics of prevailing F/OSS EHR systems and aimed to provide an understanding of the motivation, knowledge background, and characteristics of the developers. METHODS: This study identified F/OSS EHR projects on SourceForge and other websites from May to July 2014. Projects were classified and characterized by license type, downloads, programming languages, spoken languages, project age, development status, supporting materials, top downloads by country, and whether they were "certified" EHRs. Health care F/OSS developers were also surveyed using an online survey. RESULTS: At the time of the assessment, we uncovered 54 open source EHR projects, but only four of them had been successfully certified under the Office of the National Coordinator for Health Information Technology (ONC Health IT) Certification Program. In the majority of cases, the open source EHR software was downloaded by users in the United States (64.07%, 148,666/232,034), underscoring that there is a significant interest in EHR open source applications in the United States. A survey of EHR open source developers was conducted and a total of 103 developers responded to the online questionnaire. The majority of EHR F/OSS developers (65.3%, 66/101) are participating in F/OSS projects as part of a paid activity and only 25.7% (26/101) of EHR F/OSS developers are, or have been, health care providers in their careers. In addition, 45% (45/99) of developers do not work in the health care field. CONCLUSION: The research presented in this study highlights some challenges that may be hindering the future of health care F/OSS. A minority of developers have been health care professionals, and only 55% (54/99) work in the health care field. This undoubtedly limits the ability of functional design of F/OSS EHR systems from being a competitive advantage over prevailing commercial EHR systems. Open source software seems to be a significant interest to many; however, given that only four F/OSS EHR systems are ONC-certified, this interest is unlikely to yield significant adoption of these systems in the United States. Although the Health Information Technology for Economic and Clinical Health (HITECH) act was responsible for a substantial infusion of capital into the EHR marketplace, the lack of a corporate entity in most F/OSS EHR projects translates to a marginal capacity to market the respective F/OSS system and to navigate certification. This likely has further disadvantaged F/OSS EHR adoption in the United States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".