Telehealth-Delivered Medication-Assisted Treatment for Opioid Use Disorder: A Systematic Review and Meta-Analysis of Efficacy and Outcomes
Bibliographic record
Abstract
Purpose and Background The opioid crisis continues to pose one of the most urgent public health challenges in North America and globally. While Medication-Assisted Treatment (MAT)—which combines FDA-approved medications such as buprenorphine, methadone, or naltrexone with counseling and behavioral therapies—has proven effective in reducing opioid use and overdose, barriers such as stigma, provider shortages, and limited access persist, particularly in rural and underserved areas. The COVID-19 pandemic accelerated the adoption of telehealth-delivered MAT, offering new possibilities for expanding equitable access to care. However, the evidence base remains fragmented. The purpose of this project is to synthesize existing research evaluating the efficacy, utilization, and patient outcomes of telehealth-delivered MAT compared with traditional in-person treatment for Opioid Use Disorder (OUD). This systematic review and meta-analysis aims to: Quantitatively assess treatment retention, overdose reduction, and other patient outcomes in telehealth-delivered MAT. Identify population and program factors influencing intervention effectiveness. Explore research and policy gaps, particularly those related to digital health equity, broadband access, and regulatory barriers to telehealth implementation. Methods Overview This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. Databases searched: PubMed, Scopus, Web of Science, and Cochrane CENTRAL. Search timeframe: Inception to April 2024. Inclusion criteria: Studies reporting quantitative or mixed-methods data on telehealth-delivered MAT for OUD. Data management tools: EndNote (for deduplication) and Rayyan (for screening). Risk of bias tools: Newcastle-Ottawa Scale (NOS), AXIS, CASP, and TIDieR as appropriate. Meta-analysis model: Random-effects (DerSimonian–Laird) using log odds ratios and standardized mean differences. Registration: This protocol is preregistered on the Open Science Framework (OSF) to ensure transparency and reproducibility. Expected Outcomes The project is expected to: Provide pooled quantitative evidence on retention and overdose outcomes associated with telehealth MAT. Identify consistent patterns across populations, settings, and telehealth modalities. Offer clear policy recommendations supporting permanent telehealth prescribing flexibilities and integration into addiction care. Highlight key evidence gaps—such as lack of data from low- and middle-income countries, or inequities related to broadband access, digital literacy, and stigma. Impact and Knowledge Mobilization Findings will inform clinical practice, public health policy, and telehealth expansion strategies in addiction medicine. The results will be disseminated through peer-reviewed publications, conference presentations, and knowledge-mobilization partnerships with Canadian and U.S. healthcare institutions. In alignment with WHO and CDC telehealth frameworks, this project supports evidence-based digital health transformation to improve equitable access to OUD treatment.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".