Evaluation of Interventions to Reduce Opioid Prescribing for Patients Discharged From the Emergency Department
Bibliographic record
Abstract
Importance: Limiting opioid overprescribing in the emergency department (ED) may be associated with decreases in diversion and misuse. Objective: To review and analyze interventions designed to reduce the rate of opioid prescriptions or the quantity prescribed for pain in adults discharged from the ED. Data Sources: MEDLINE, Embase, CINAHL, PsycINFO, and Cochrane Controlled Register of Trials databases and the gray literature were searched from inception to May 15, 2020, with an updated search performed March 6, 2021. Study Selection: Intervention studies aimed at reducing opioid prescribing at ED discharge were first screened using titles and abstracts. The full text of the remaining citations was then evaluated against inclusion and exclusion criteria by 2 independent reviewers. Data Extraction and Synthesis: Data were extracted independently by 2 reviewers who also assessed the risk of bias. Authors were contacted for missing data. The main meta-analysis was accompanied by intervention category subgroup analyses. All meta-analyses used random-effects models, and heterogeneity was quantified using I2 values. Main Outcomes and Measures: The primary outcome was the variation in opioid prescription rate and/or prescribed quantity associated with the interventions. Effect sizes were computed separately for interrupted time series (ITS) studies. Results: Sixty-three unique studies were included in the review, and 45 studies had sufficient data to be included in the meta-analysis. A statistically significant reduction in the opioid prescription rate was observed for both ITS (6-month step change, -22.61%; 95% CI, -30.70% to -14.52%) and other (odds ratio, 0.56; 95% CI, 0.45-0.70) study designs. No statistically significant reduction in prescribed opioid quantities was observed for ITS studies (6-month step change, -8.64%; 95% CI, -17.48% to 0.20%), but a small, statistically significant reduction was observed for other study designs (standardized mean difference, -0.30; 95% CI, -0.51 to -0.09). For ITS studies, education, policies, and guideline interventions (6-month step change, -33.31%; 95% CI, -39.67% to -26.94%) were better at reducing the opioid prescription rate compared with prescription drug monitoring programs and laws (6-month step change, -11.18%; 95% CI, -22.34% to -0.03%). Most intervention categories did not reduce prescribed opioid quantities. Insufficient data were available on patient-centered outcomes such as pain relief or patients' satisfaction. Conclusions and Relevance: This systematic review and meta-analysis found that most interventions reduced the opioid prescription rate but not the prescribed opioid quantity for ED-discharged patients. More studies on patient-centered outcomes and using novel approaches to reduce the opioid quantity per prescription are needed. Trial Registration: PROSPERO Identifier: CRD42020187251.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".