Physician Assessment and Feedback During Quality Circle to Reduce Low-Value Services in Outpatients: a Pre-Post Quality Improvement Study
Bibliographic record
Abstract
BACKGROUND: The impact of the Choosing Wisely (CW) campaign is debated as recommendations alone may not modify physician behavior. OBJECTIVE: The aim of this study was to assess whether behavioral interventions with physician assessment and feedback during quality circles (QCs) could reduce low-value services. DESIGN AND PARTICIPANTS: Pre-post quality improvement intervention with a parallel comparison group involving outpatients followed in a Swiss-managed care network, including 700 general physicians (GPs) and 150,000 adult patients. INTERVENTIONS: Interventions included performance feedback about low-value activities and comparison with peers during QCs. We assessed individual physician behavior and healthcare use from laboratory and insurance claims files between August 1, 2016, and October 31, 2018. MAIN MEASURES: Main outcomes were the change in prescription of three low-value services 6 months before and 6 months after each intervention: measurement of prostate-specific antigen (PSA) and prescription rates of proton pump inhibitors (PPIs) and statins. KEY RESULTS: Among primary care practices, a QC intervention with physician feedback and peer comparison resulted in lower rates of PPI prescription (pre-post mean prescriptions per GP 25.5 ± 23.7 vs 22.9 ± 21.4, p value<0.01; coefficient of variation (Cov) 93.0% vs 91.0%, p=0.49), PSA measurement (6.5 ± 8.7 vs 5.3 ± 6.9 tests per GP, p<0.01; Cov 133.5% vs 130.7%, p=0.84), as well as statins (6.1 ± 6.8 vs 5.6 ± 5.4 prescriptions per GP, p<0.01; Cov 111.5% vs 96.4%, p=0.21). Changes in prescription of low-value services among GPs who did not attend QCs were not statistically significant over this time period. CONCLUSION: Our results demonstrate a modest but statistically significant effect of QCs with educative feedback in reducing low-value services in outpatients with low impact on coefficient of variation. Limiting overuse in medicine is very challenging and dedicated discussion and real-time review of actionable data may help.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".