Clinician Views of an Opioid Prescribing Report with Peer Comparisons and Patient-Reported Outcomes
Bibliographic record
Abstract
Providing feedback to clinicians on their prescribing is a promising approach to right-sizing opioid prescriptions. The present research investigated the perceived acceptability, appropriateness, helpfulness, and areas for improvement of a monthly report providing surgical clinicians feedback on their postoperative opioid prescribing relative to guidelines, peer prescribing, and patient-reported pills taken, as well as on patient-reported ability to manage pain. Between January and May 2023, surgeons, advanced practice providers, and residents who recently received these reports as part of a health system quality improvement intervention completed a survey (n = 38) or interview (n = 8). Mean (SD) acceptability of the prescribing report was 4.2 (0.8), and appropriateness was 4.2 (0.8); appropriateness varied by clinical role. All features of the report were rated as "very" or "extremely" helpful by a majority of respondents. Interviewees wished for fuller explanations, real-time updates, and improved accuracy. These findings can inform the design of clinician feedback in learning health systems.
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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 | 0.023 | 0.179 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".