PLOS Digital Health
Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial
We developed and deploy a real‑time, electronic health record‑integrated machine learning phenotype to identify emergency department patients with opioid use disorder for prospective clinical trial screening and buprenorphine initiation. We conducted a multi‑phase study across three emergency departments in a single United States health system from 2014 to 2025. Using visit‑level data available at or before triage, we trained a random‑forest classifier to estimate opioid use disorder risk and embedded scoring in th …