Governance and Sustainability of an Open Source Electronic Health Record: An Interpretive Case Study of OpenDolphin in Japan
Bibliographic record
Abstract
Electronic Health Records (EHRs) are at the heart of reforms aiming for improving the efficiency and quality of citizens healthcare services. Although there is still some skepticism, open source (OS) EHR is a growing phenomenon in health informatics. Given the widespread adoption of OS software (OSS) in several domains, including operating systems, and enterprise systems, the repeated shortfalls faced by healthcare organizations with dominant proprietary EHRs create an opportunity for other alternatives, such as OSS to demonstrate their abilities in addressing these well-documented problems, including inflexibility, high costs, and low interoperability. However, scholars have expressed extensive concerns about the sustainability of OS EHR. Recognizing that OSS project sustainability relies on their governance arrangements, this case study reports on the evolution of the governance and sustainability of a Japanese OS EHR project and provides rich insights to other open source EHR initiative stakeholders, including physicians, developers, researchers, and policymakers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".