Successful implementation of a quality improvement bundle to reduce opioid overprescribing following total hip and knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: Opioid overprescribing is commonplace after total hip (THA) and total knee arthroplasty (TKA). Preliminary data demonstrated that approximately 32% of the opioids prescribed at discharge from our hospital following THA and TKA remain unused. This is a concern given that unused prescribed opioids are available for diversion and may result in misuse and abuse. METHODS: Pre-intervention data were collected between 1 November 2018 and 10 December 2018. An intervention bundle was then introduced, including education of patients and providers, a standardised pain management algorithm and an autopopulated discharge prescription. The aim of this quality improvement initiative was to reduce the amount of opioid (average oral morphine equivalents (OME)) dispensed (based on the discharge prescription provided) following THA and TKA at our institution by 15% by 1 April 2019. DESIGN: Using an interrupted time series design, the outcome measure was the amount of opioid (OME) dispensed from the discharge prescription provided. Process measures included the percentage of autopopulated discharge prescriptions, the percentage of patients receiving education at discharge and the percentage of nurses and residents receiving standardised education. Balancing measures included patient satisfaction with postoperative pain management, and the percentage of patients filling the second half of the part-fill or requiring a subsequent opioid prescription. RESULTS: With 600 patients identified, mean OME dispensed at discharge was reduced by 26.3% (from 522.2 to 384.9 mg) after our interventions started. Utilisation of autopopulated part-fill prescriptions was 95.8%. There was no change in patient satisfaction nor in the proportion of patients requiring an additional opioid prescription post-intervention. Only 39% of patients filled the second half of the part-fill prescription post-intervention. CONCLUSIONS: Mean OME dispensed at discharge per patient was reduced with no change in patient satisfaction after introduction of the intervention bundle.
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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 | 0.030 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".