Interventions to improve access to opioid agonist therapy in acute hospitals: A scoping review
Bibliographic record
Abstract
Many people who use illicit opioids have negative experiences when admitted to hospital, which is partly due to poor availability of opioid agonist therapy (OAT). We conducted a scoping review of interventions to increase access OAT to for hospital patients, with searches of MEDLINE, EMBASE, PsychINFO, and CINAHL for evaluations published before 29 July 2024. We followed a registered protocol (identifier: CRD42022313237). We included interventions in acute inpatient or emergency department settings, and extracted intervention characteristics, location, evaluation design and quality, and evidence for effectiveness. We included 57 studies; 50 from the United States, six from Canada, and one from the UK. Fifty-one were published in 2015 or later. We identified three intervention classes: (a) pathways to initiate OAT in emergency departments, entailing screening patients or training staff to identify withdrawal, initiating buprenorphine, and supported referrals (26 studies); (b) multidisciplinary 'addiction consult teams', which provide substance-related care across hospital departments, advise primary medical teams on issues such as pain relief and withdrawal management, and support patients with discharge and onward care (18 studies); and (c) Interventions that build capacity of general clinical teams to provide OAT to inpatients, including protocols to identify patients who need OAT, multidisciplinary patient review, and training/clinical education (13 studies). Most interventions included multiple components, and the most common were clinical education and measures to improve continuity of OAT after discharge, such as bridge prescriptions and supported referrals to community prescribers. Almost all studies concluded that interventions were effective, however evaluation methods were generally weak and most used before/after or case series designs. Efforts to improve OAT in acute hospitals emerged recently in North America and focus on addiction consult teams and initiation of buprenorphine in emergency departments. Although formal evaluation is weak, these models may represent starting points for national policy and larger research programmes.
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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 | 0.022 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".