Implementing Programs to Initiate Buprenorphine for Opioid Use Disorder Treatment in High-Need, Low-Resource Emergency Departments: A Nonrandomized Controlled Trial
Bibliographic record
Abstract
STUDY OBJECTIVE: We hypothesized that implementation facilitation would enable us to rapidly and effectively implement emergency department (ED)-initiated buprenorphine programs in rural and urban settings with high-need, limited resources and dissimilar staffing structures. METHODS: This multicenter implementation study employed implementation facilitation using a participatory action research approach to develop, introduce, and refine site-specific clinical protocols for ED-initiated buprenorphine and referral in 3 EDs not previously initiating buprenorphine. We assessed feasibility, acceptability, and effectiveness by triangulating mixed-methods formative evaluation data (focus groups/interviews and pre/post surveys involving staff, patients, and stakeholders), patients' medical records, and 30-day outcomes from a purposive sample of 40 buprenorphine-receiving patient-participants who met research eligibility criteria (English-speaking, medically stable, locator information, nonprisoners). We estimated the primary implementation outcome (proportion receiving ED-initiated buprenorphine among candidates) and the main secondary outcome (30-day treatment engagement) using Bayesian methods. RESULTS: Within 3 months of initiating the implementation facilitation activities, each site implemented buprenorphine programs. During the 6-month programmatic evaluation, there were 134 ED-buprenorphine candidates among 2,522 encounters involving opioid use. A total of 52 (41.6%) practitioners initiated buprenorphine administration to 112 (85.1%; 95% confidence interval [CI] 79.7% to 90.4%) unique patients. Among 40 enrolled patient-participants, 49.0% (35.6% to 62.5%) were engaged in addiction treatment 30 days later (confirmed); 26 (68.4%) reported attending one or more treatment visits; there was a 4-fold decrease in self-reported overdose events (odds ratio [OR] 4.03; 95% CI 1.27 to 12.75). The ED clinician readiness increased by a median of 5.02 (95% CI: 3.56 to 6.47) from 1.92/10 to 6.95/10 (n(pre)=80, n(post)=83). CONCLUSIONS: The implementation facilitation enabled us to effectively implement ED-based buprenorphine programs across heterogeneous ED settings rapidly, which was associated with promising implementation and exploratory patient-level outcomes.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".