Teaching Quality Improvement and Patient Safety to Trainees: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To systematically review published quality improvement (QI) and patient safety (PS) curricula for medical students and/or residents to (1) determine educational content and teaching methods, (2) assess learning outcomes achieved, and (3) identify factors promoting or hindering curricular implementation. METHOD: Data sources included Medline (to January 2009), EMBASE, HealthSTAR, and article bibliographies. Studies selected reported curricula outlining specific educational content and teaching format. For articles with an evaluative component, the authors abstracted methodological features, such as study design. For all articles, they conducted a thematic analysis to identify factors influencing successful implementation of the included curricula. RESULTS: Of 41 curricula that met the authors' criteria, 14 targeted medical students, 24 targeted residents, and 3 targeted both. Common educational content included continuous QI, root cause analysis, and systems thinking. Among 27 reports that included an evaluation, curricula were generally well accepted. Most curricula demonstrated improved knowledge. Thirteen studies (32%) successfully implemented local changes in care delivery, and seven (17%) significantly improved target processes of care. Factors that affected the successful curricular implementation included having sufficient numbers of faculty familiar with QI and PS content, addressing competing educational demands, and ensuring learners' buy-in and enthusiasm. Participants in some curricula also commented on discrepancies between curricular material and local institutional practice or culture. CONCLUSIONS: QI and PS curricula that target trainees usually improve learners' knowledge and frequently result in changes in clinical processes. However, successfully implementing such curricula requires attention to a number of learner, faculty, and organizational factors.
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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 | 0.022 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".