Postoperative Opioid Prescription Reduction Strategy in a Regional Healthcare System
Bibliographic record
Abstract
BACKGROUND: The CDC reported in 2017 that the largest increments in probability of continued use were observed after days 5 and 31 on opioid therapy. This study demonstrates the correlation between a system-wide pain management and opioid stewardship effort with reductions in discharge prescriptions for elective surgical patients. STUDY DESIGN: Discharge prescriptions were monitored through the electronic health record. Baseline prescribing patterns were established for the first quarter of 2018, preceding the first intervention in the multipronged opioid reduction initiative. Beginning in the second quarter of 2018, a series of pain management and opioid stewardship educational conferences were provided. Enhanced Recovery after Surgery protocols were simultaneously implemented system-wide. In the third quarter of 2018, a quality metric linked to compensation rewarded surgeons for limiting postoperative discharge prescriptions to 5 or fewer days. Opioid prescriptions were compared by quarter from January 2018 to March 2019 using chi-square and Kruskal-Wallis test with significance of p < 0.05. RESULTS: There were 31,814 patients who underwent elective surgical procedures during the study period. At baseline, the rate of postoperative opioid prescriptions of 5 or fewer days was 81%. This rate increased to 82%, 86%, 89%, and 92% in each successive quarter (p < 0.0001 for quarters 3 to 5). CONCLUSIONS: A system-wide, multipronged pain management and opioid reduction program significantly reduced opioid discharge prescriptions written for more than 5 days. This approach can serve as a model for other healthcare systems attempting to reduce opioid prescribing and combat the opioid crisis in the US.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".