Clinical performance comparators in audit and feedback: a review of theory and evidence
Bibliographic record
Abstract
BACKGROUND: Audit and feedback (A&F) is a common quality improvement strategy with highly variable effects on patient care. It is unclear how A&F effectiveness can be maximised. Since the core mechanism of action of A&F depends on drawing attention to a discrepancy between actual and desired performance, we aimed to understand current and best practices in the choice of performance comparator. METHODS: We described current choices for performance comparators by conducting a secondary review of randomised trials of A&F interventions and identifying the associated mechanisms that might have implications for effective A&F by reviewing theories and empirical studies from a recent qualitative evidence synthesis. RESULTS: We found across 146 trials that feedback recipients' performance was most frequently compared against the performance of others (benchmarks; 60.3%). Other comparators included recipients' own performance over time (trends; 9.6%) and target standards (explicit targets; 11.0%), and 13% of trials used a combination of these options. In studies featuring benchmarks, 42% compared against mean performance. Eight (5.5%) trials provided a rationale for using a specific comparator. We distilled mechanisms of each comparator from 12 behavioural theories, 5 randomised trials, and 42 qualitative A&F studies. CONCLUSION: Clinical performance comparators in published literature were poorly informed by theory and did not explicitly account for mechanisms reported in qualitative studies. Based on our review, we argue that there is considerable opportunity to improve the design of performance comparators by (1) providing tailored comparisons rather than benchmarking everyone against the mean, (2) limiting the amount of comparators being displayed while providing more comparative information upon request to balance the feedback's credibility and actionability, (3) providing performance trends but not trends alone, and (4) encouraging feedback recipients to set personal, explicit targets guided by relevant information.
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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 | 0.233 | 0.498 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".