Strategies for enabling physician leadership and involvement in quality improvement: a scoping review
Bibliographic record
Abstract
Background: The importance of physician advocacy and leadership in quality improvement (QI) in health care is well recognized, but achieving physician involvement is challenging. The purpose of this scoping review was to describe strategies used in physician-led QI models/approaches that include learning about the science of improvement and may enable physician QI capability, participation, and leadership. Methods: Articles were identified through electronic searches of MEDLINE, Embase, CINAHL, and Scopus, and reference lists were reviewed. For each model/approach, descriptions of strategies were extracted and the frequency of each strategy was determined. Thematic analysis was conducted. Results: Eleven articles representing nine unique models/approaches were included. From these, 20 enabler strategies were identified, and eight themes emerged: dedicated support staff; operational alignment and leader support; evidence-informed care; sharing QI to encourage QI; financial investment; formal QI leader role and responsibility; physician mentorship; and QI capability. No model/approach included all the strategies, and the number of strategies aligned with each theme varied. Heterogeneity in reporting physician-led QI approaches and broad use of the term “physician-led” increased search complexity. Conclusion: Comprehensive models/approaches that encourage physicians to participate in and lead QI while learning the science of improvement have not yet been developed. Research on physician QI participation and strategy evaluation, including effectiveness, is required. This review offers a road map of enabler strategies that can be used to support future models.
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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 | 0.054 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".