A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data

Background: Buprenorphine and methadone are effective medications for opioid use disorder (OUD) but remain underused, particularly when specialists are not leading care decisions. Objective: We developed a predictive model to guide treatment selection for OUD. Methods: Models predicted the probability of treatment response for each medication, which we defined as the absence of an adverse outcome during hospitalization and within 90 days of hospital discharge. Models considered electronic health record (EHR) and ZIP-level data. We constructed generalized linear regression, random forest, gradient boosted machines, and deep learning models and tested different combinations of EHR and ZIP-level data using early and late fusion methods. Results: EHR-only models performed better than ZIP-only models did. ZIP-level data did not significantly improve the performance of EHR-only models. Models consistently recommended buprenorphine over methadone. Conclusion: Future work should explore different approaches to modeling OUD treatment response and capturing relevant social and external factors.