Actioning the Learning Health System: An applied framework for integrating research into health systems
Bibliographic record
Abstract
Health systems across the world experience pervasive gaps in the speed with which high quality evidence is generated, implemented and refined. A Learning Health System (LHS) approach that blends research with health care operations is to eliminate or reduce delays. This paper builds on existing LHS frameworks to deepen our practical understanding of the research-health systems operations interface and to provide actionable insights on how to realize a LHS in practice. We present an LHS action framework that describes how research and health care operations are linked and enacted in a comprehensive LHS approach to advance population health and health equity. Health systems seeking to implement an LHS approach can use this framework to identify capabilities necessary to enact the learning elements, including key questions and methods, to ensure a systematic approach to learning and achieving equity-centered quadruple aim metrics.
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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 | 0.105 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".