A unifying framework for improving health care
Bibliographic record
Abstract
The quality health care around world is suboptimal. To improve the quality of contemporary health care delivery, advocates have proposed a number of scientific and technical initiatives. All these initiatives, however, have arisen and continue to operate in siloes, resulting in confusion and incommensurability among those concerned with health care improvement. Participants in the quality improvement (QI) space typically stress their own, often narrow, perspective, failing to place QI in context or to acknowledge other approaches. In order to improve delivery of health care, the following is required: Provide a unifying framework for improving health care. We argue this is best done under a Health System Science (HSS) framework but with full understanding that the fundamental principles of HSS are rooted in evidence-based medicine (EBM) and decision sciences. Understand that QI initiatives are fundamentally local activities. Hence, incentivizing bottom-up, local QI initiatives would improve health care delivery to a far greater extent than the current top-down initiatives undertaken in a response to various regulatory mandates. Akin to the "Choosing Wisely" initiative, which challenged professional societies, each institution should identify (a) the extent to which its practices are evidence-based and (b) the top 5 health care practices or interventions that, at a given institution, represent overuse, underuse, or misuse/error or undermine clinicians' efforts to deliver kind and empathic care. Providing a framework that can unify the current patchwork of the initiatives would help create a common basis to help align all the existing QI efforts. In addition, thinking small (at local level) may lead to health care quality improvements that national initiatives (thinking big), focused on regulation, competition, or legal requirements, have failed to achieve.
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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 | 0.061 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.010 | 0.068 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.010 | 0.012 |
| 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".