Engaging Residents and Fellows to Improve Institution-Wide Quality
Bibliographic record
Abstract
PURPOSE: Teaching hospitals strive to engage physicians in quality improvement (QI), and graduate medical education (GME) programs must promote trainee competence in systems-based practice (SBP). The authors developed a QI incentive program that engages residents and fellows, providing them with financial incentives to improve quality while simultaneously gaining SBP experience. In this study, they describe and evaluate success in meeting goals set during the program's first six years. METHOD: During fiscal years (FYs) 2007-2012, QI project goals for all or specific training programs were set collaboratively with residents and fellows at the University of California, San Francisco (UCSF). Data were collected from administrative databases, via chart abstraction, or through independently designed techniques. RESULTS: Approximately 5,275 residents and fellows were eligible and participated in the program. A total of 55 projects were completed. Among the 18 all-program projects, goals were achieved for 11 (61%) in three domains: patient satisfaction, quality/safety, and operation/utilization. Among the 37 program-specific projects, goals were achieved for 28 (76%) in four categories: patient-level interventions, enhanced communication, workflow improvements, and effective documentation. Residents and fellows earned an average of $800 in bonuses/FY for achieving these goals. CONCLUSIONS: Thousands of residents and fellows across disciplines participated in real-life, real-time QI during the program's first six years. Participation provided an experience that may promote SBP competence and resulted in improved quality of care across the UCSF Medical Center. Similar programs may assist teaching hospitals and GME programs in meeting current and future QI and training mandates.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".