Implementation of a regional quality improvement collaborative to improve care of people living with opioid use disorder in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: Although opioid agonist therapy is effective in treating opioid use disorders (OUD), retention in opioid agonist therapy is suboptimal, in part, due to quality of care issues. Therefore, we sought to describe the planning and implementation of a quality improvement initiative aimed at closing gaps in care for people living with OUD through changes to workflow and care processes in Vancouver, Canada. METHODS: The Best-practice in Oral Opioid agoniSt Therapy (BOOST) Collaborative followed the Institute for Healthcare Improvement's Breakthrough Series Collaborative methodology over 18-months. Teams participated in a series of activities and events to support implementing, measuring, and sharing best practices in OAT and OUD care. Teams were assigned monthly implementation scores to monitor their progress on meeting Collaborative aims and implementing changes. RESULTS: Seventeen health care teams from a range of health care practices caring for a total of 4301 patients with a documented diagnosis of OUD, or suspected OUD based on electronic medical record chart data participated in the Collaborative. Teams followed the Breakthrough Series Collaborative methodology closely and reported monthly on a series of standardized process and outcome indicators. The majority of (59%) teams showed some improvement throughout the Collaborative as indicated by implementation scores. CONCLUSIONS: Descriptive data from the evaluation of this initiative illustrates its success. It provides further evidence to support the implementation of quality improvement interventions to close gaps in OUD care processes and treatment outcomes for people living with OUD. This system-level approach has been spread across British Columbia and could be used by other jurisdictions facing similar overdose crises.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".