Towards Open Collaborative Health Informatics - The Role of Free/Libre Open Source Principles
Bibliographic record
Abstract
OBJECTIVES: : To analyze the contribution of Free/Libre Open Source Software in health care (FLOSS-HC) and to give perspectives for future developments. METHODS: The paper summarizes FLOSS-related trends in health care as anticipated by members of the IMIA Open Source Working Group. Data were obtained through literature review and personal experience and observations of the authors in the last two decades. A status quo is given by a frequency analysis of the database of Medfloss.org, one of the world's largest platforms dedicated to FLOSS-HC. The authors discuss current problems in the field of health care and finally give a prospective roadmap, a projection of the potential influences of FLOSS in health care. RESULTS: FLOSS-HC already exists for more than 2 decades. Several projects have shown that FLOSS may produce highly competitive alternatives to proprietary solutions that are at least equivalent in usability and have a better total cost of ownership ratio. The Medfloss.org database currently lists 221 projects of diverse application types. CONCLUSIONS: FLOSS principles hold a great potential for addressing several of the most critical problems in health care IT. The authors argue that an ecosystem perspective is relevant and that FLOSS principles are best suited to create health IT systems that are able to evolve over time as medical knowledge, technologies, insights, workflows etc. continuously change. All these factors that inherently influence the development of health IT systems are changing at an ever growing pace. Traditional models of software engineering are not able to follow these changes and provide up-to-date systems for an acceptable cost/value ratio. To allow FLOSS to positively influence Health IT in the future a "FLOSS-friendly" environment has to be provided. Policy makers should resolve uncertainties in the legal framework that disfavor FLOSS. Certification procedures should be specified in a way that they do not raise additional barriers for FLOSS.
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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 | 0.092 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".