Harnessing telemedicine to deliver patient-centered opioid agonist treatment within a community-based harm reduction service: a mixed methods evaluation
Bibliographic record
Abstract
BACKGROUND: Patient-centered approaches may facilitate retention in opioid agonist treatment (OAT) but are challenging to implement in rigid or stigmatizing service contexts. We evaluate a telemedicine program delivering flexible, patient-centered OAT from a community-based harm reduction setting in Montreal, Canada. METHODS: An OAT clinic was established in a community-based harm reduction setting with hospital-based addiction medicine services delivered remotely via telemedicine. Community workers screened clients, established telemedicine connections, and offered holistic patient follow-up. The medical team offered individualized OAT and other health services. Patients chose between treatment with methadone, buprenorphine/naloxone, or slow-release oral morphine. Hydromorphone co-prescription was also available. Effectiveness was assessed via longitudinal chart review and semi-structured interviews (n = 20). A convergent mixed method design was used to quantify retention rates and blood borne infection care up to 12 month follow up, and to examine patient reported program experiences. RESULTS: Sixty-nine patients (46 men, 23 women; median age 38) initiated OAT between April 2020-March 2022. Most (96%) were injecting opioids, 56% were unstably housed, and 71% reported prior OAT. Patients typically initiated treatment with methadone (54%) or slow-release oral morphine (35%); 78% also received hydromorphone. Continuous retention in the first OAT episode was 83% at one month, 74% at three months, and 54% at 12 months. Disregarding prior treatment interruptions, 71% of patients were receiving OAT at 12 months. Most patients were assessed for HIV (77%) and HCV (78%), and 13/15 confirmed as HCV-positive initiated antiviral treatment. Trust, respect, and the alignment of practices with patient-centered care and harm reduction principles were critical to success. Integrating treatment within a community-based harm reduction setting enhanced accessibility and care coordination, and created a welcoming service environment. Diverse medication options, collaborative treatment planning, and a non-judgmental/non-punitive approach were key to developing positive therapeutic relationships. CONCLUSION: Our community-based telemedicine program presents a novel framework for OAT delivery that efficiently bridges the health and community sectors. Working collaboratively around the patient, program partners leveraged their strengths to improve treatment experiences and promote retention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".