AI-powered clinical tool aims to prevent opioid disorder relapse

An AI-powered clinical support tool will help prevent relapse in patients receiving buprenorphine treatment for opioid use disorder — a condition that affects hundreds of thousands of people in America every year.

The PROTECT tool, devised by researchers at the University of Florida and the University of Pittsburgh with a $3.6 million grant from the National Institutes of Health and the National Institute on Drug Abuse, uses machine learning algorithms to identify buprenorphine patients who are at high risk of relapsing and provides recommendations for next steps.

Mahmudul Hasan, Ph.D., an assistant professor in the UF College of Pharmacy Department of Pharmaceutical Outcomes and Policy, leads the UF-based team developing PROTECT.

“Relapse risk isn’t a single lab value; it’s a pattern hidden across medical charts and in the social context. Our goal is to surface that pattern in time for busy primary care teams to act, so prevention becomes proactive rather than reactive,” said Mahmudul Hasan, Ph.D., an assistant professor in the UF College of Pharmacy Department of Pharmaceutical Outcomes and Policy with a joint appointment in the UF Warrington College of Business Information Systems and Operations Management Department. “PROTECT can flag early, subtle patterns and show an individual’s top risk factors, so teams know who is at risk, why and what to do next.”