Physician Self-Audit: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Self-audit involves self-collection of personal performance data, reflection on gaps between performance and standards, and development and implementation of learning or quality improvement plans by individual care providers. It appears to stimulate learning and quality improvement, but few physicians engage in self-audit. The purpose of this study was to identify how self-audit has been operationalized; factors influencing self-audit conduct and outcomes, including program design; and issues warranting further research. METHODS: A systematic review of quantitative and qualitative studies was undertaken. Two individuals independently reviewed searches of indexed literature databases, tables of contents, and references of eligible studies. Data were extracted and tabulated to describe the nature and impact of self-audit programs. RESULTS: Six studies evaluated the impact of self-audit programs. No program was based on a model or theory that informed its design. All studies showed improved compliance with care delivery guidelines and/or improved patient outcomes, although these findings were largely self-reported. Programs varied so features associated with benefit could not be identified. DISCUSSION: Overall there is a need for guidance on all aspects of self-audit for both participants and leaders. This guidance would be useful to educators, professional associations, and medical certification bodies to plan, develop, implement, evaluate, and support self-audit programs. Further research should aim at developing training programs and tools that address and evaluate a variety of competencies across different disciplines using more rigorous research designs, including both quantitative and qualitative approaches.
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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 | 0.046 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.034 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| 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".