A Peer Data Benchmarking Intervention to Reduce Opioid Overprescribing: A Randomized Controlled Trial
Bibliographic record
Abstract
BackgroundDriving physician behavior change has been an elusive goal for quality improvement efforts aimed at reducing low-value care. We proposed the use of “nudge” interventions at the surgeon level in order to reduce post-surgical opioid overprescribing in accordance with consensus guidelines.MethodsWe used 2017 Medicare data to identify outlier surgeons. A peer data benchmarking report that showed each surgeon the average number of opioid tablets they prescribed for an open inguinal hernia repair procedure from January 1, 2017 to December 31, 2017. We conducted a 1:1 randomized controlled trial providing outlier surgeons a report of their opioid prescribing patterns for a standard operation compared to the national average and prescribing guidelines.ResultsThere were 489 surgeons randomized to the intervention, of which 180 (36.8%) had data in the post-intervention period. Data was available for 87 surgeons in the intervention group and 93 surgeons in the control group. 97.7% of surgeons in the intervention group reduced their opioid prescribing pattern compared to 95.7% in the control group. Surgeons who received the data benchmarking report intervention prescribed 14.3% less opioids than surgeons in the control group (10.54 (SD 5.34) vs. 12.30 (SD 6.02), <i>P</i> = .04). The intervention was associated with a 1.83 lower mean number of opioid tablets prescribed per patient in the multivariable linear regression model after controlling for other factors (Intervention group vs. control group 95% CI [−3.61, −.04], <i>P</i> = .04).DiscussionThe implementation of a peer data benchmarking intervention can drive physician behavior change towards high-value care.
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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 | 0.046 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.339 | 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; both teacher heads 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".