Evaluation of a hospital-based opioid stewardship program on high-risk opioid prescribing in a Canadian setting: an interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: High-risk opioid prescribing (e.g., high daily dose opioids, concurrent opioid-sedatives) is prevalent in hospitals and linked to adverse outcomes. Opioid stewardship programs (OSP) have the potential to reduce high-risk opioid prescribing through audit-and-feedback recommendations. METHODS: We evaluated an audit-and-feedback based OSP implemented in January 2020 at a Vancouver, Canada tertiary care hospital using interrupted time series analysis. An electronic health record (EHR) system with computerized provider order entry (CPOE) was simultaneously operationalized. The main outcome was: any high-risk opioid prescribing (based on 10 evidence-based indicators), including high daily dose of morphine milligram equivalent (MME) prescribing (> 90MME), long opioid prescription duration (> 5 days post-admission), and concurrent opioid-sedative prescribing. RESULTS: Between January 2018 and March 2022, 5,477 active opioid patient encounters were included. While no significant change occurred in overall high-risk opioid prescribing post-OSP (p > 0.05), a significant reduction was seen in the level of high daily dose of MME prescriptions (estimate: -0.044; 95% confidence interval [CI]: -0.082, -0.006). Conversely, the trend in long opioid duration increased (estimate: 0.006; 95%CI: 0.000, 0.011), likely due to the removal of automatic stop dates with the implementation of the EHR with CPOE. Post-OSP intervention, we initially saw an acute increase in concurrent opioid-sedative prescriptions (estimate: 0.013; 95%CI: 0.005, 0.020). A benzodiazepine ordering intervention implemented in May 2021 reversed this trend, reducing both the level (estimate: 0.874; 95%CI: 0.374, 1.375) and slope (estimate: -0.022, 95%CI: -0.034, -0.011) of concurrent prescriptions. CONCLUSION: The implementation of a new EHR concordant with that of the OSP may have impacted our study's results. While our research suggests the OSP reduced high-dose opioid prescribing, other indicators impacted by the EHR system did not benefit as highly from the OSP. Nevertheless, the OSP proved able to rapidly respond to unintended consequences by introducing interventions to reduce concurrent opioid and sedative prescribing.
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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 | 0.025 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".