Creation of a telehealth addiction consultation service at a rural hospital: a case study
Bibliographic record
Abstract
BACKGROUND: Rural communities face significant barriers to accessing substance use disorder (SUD) treatment, resulting in gaps in care and increased rates of opioid-related overdose deaths. Hospital-based Addiction Consult Services (ACS) improve outcomes for patients with SUD, but rural hospitals often lack these services. CASE PRESENTATION: The Community Addiction Consult (CAC) service was established at a rural hospital in western Massachusetts to address this gap. CAC was designed by a community coalition comprised of a diverse cross-section of the community in which the hospital is based, using opioid-overdose data from the region to inform their decisions. Using a telehealth model, the CAC provided evidence-based treatments to support hospital staff treating patients with opioid use disorder (OUD) or requiring addiction-related care. From April 2023 through December 2023, the CAC provided 36 consults, facilitating increased access to medications for opioid use disorder (MOUD), and enhancing provider confidence in treating people who use drugs (PWUD) and initiating MOUD. An average of 22 patients received MOUD as inpatients monthly, and 11 emergency department patients received MOUD monthly. The CAC team also implemented training sessions, and an anti-stigma campaign to familiarize hospital staff with harm reduction principles and person-centered care strategies to foster a more supportive treatment environment for PWUD. CONCLUSIONS: The Community Addiction Consult service demonstrates the feasibility and efficacy of a telehealth Addiction Consult Service model. Paired with staff trainings, such a model can bridge the gaps in rural addiction care. By leveraging local expertise and data-driven approaches, this model offers a scalable, equitable solution to improving access to substance use disorder treatment in rural settings.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".