Political, Policy and Social Barriers to Health System Interoperability: Emerging Opportunities of Web 2.0 and 3.0
Bibliographic record
Abstract
Achieving effective health informatics interoperability in a fragmented and uncoordinated health system is by definition not possible. Interoperability requires the simultaneous integration of health care processes and information across different types and levels of care (systems thinking). The fundamental argument of this paper is that information system interoperability will remain an unfulfilled hope until health reforms effectively address the governance (accountability), structural and process barriers to interoperability of health care delivery. The ascendency of Web 2.0 and 3.0, although still unproven, signals the opportunity to accelerate patients' access to health information and their health record. Policy suggestions for simultaneously advancing health system delivery and information system interoperability are posited.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".