Impact of an emergency department opioid prescribing guideline on emergency physician behaviour and incidence of overdose in the Saskatoon Health Region: a retrospective pre–post implementation analysis
Bibliographic record
Abstract
Background: Deaths related to opioid overdoses are increasing in North America, with the emergency department being identified as a potential contributor toward this epidemic. Our goal was to determine whether a departmental guideline for the prescribing of restricted medications resulted in a reduction in opioids prescribed in a Canadian setting, with a secondary objective of determining the impact on local overdose frequency. Methods: We conducted a retrospective analysis of the prescribing habits of emergency department physicians in 3 hospitals in the Saskatoon Health Region, Saskatchewan, before (Nov. 1, 2015, to Apr. 30, 2016) and after (Nov. 1, 2016, to Apr. 30, 2017) implementation of a guideline in September 2016 for the prescribing of restricted medications. We quantified opioids prescribed per hour worked and per patient seen. We performed Student paired 2-tailed t tests for both individual drug formulations and the combined total morphine equivalents. Results: Thirty-two emergency department physicians were included. We found a decrease of 31.1% in opioids prescribed, from 10.36 morphine milligram equivalents (MME) per patient seen to 7.14 MME per patient seen (absolute change −3.22 MME, 95% confidence interval −4.81 to −1.63 MME). Over the same period, we found no change in prehospital naloxone use and a modest increase in the amount of naloxone dispensed by emergency department pharmacies. There was no decrease in the number of overdoses after guideline implementation. Interpretation: Implementation of a guideline for the prescribing of restricted medications in a Canadian emergency department setting was associated with a decrease in the quantity of opioids prescribed but not in the number of overdoses. This finding suggests that the emergency department is unlikely the source of opioids used in acute overdose, although emergency department opioid prescriptions cannot be ruled out as a risk factor for opioid use disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".