A new NIH-supported clinical trial has found that an artificial intelligence (AI) screening tool can successfully identify hospitalized adults at risk for opioid use disorder and significantly reduce their chances of being readmitted to the hospital. The study, published in *Nature Medicine*, shows the AI tool is just as effective as traditional healthcare provider-led consultations—and potentially more scalable.
Developed by researchers at the University of Wisconsin School of Medicine and Public Health, the AI system was designed to monitor patient records in real time and alert providers when a patient might benefit from addiction medicine consultation. Compared to provider-only screenings, patients flagged by the AI system had 47% lower odds of being readmitted within 30 days. That reduction translated into nearly $109,000 in healthcare savings over the eight-month study period.
“AI has the potential to strengthen implementation of addiction treatment while optimizing hospital workflow and reducing healthcare costs,” said Dr. Nora D. Volkow, director of the NIH’s National Institute on Drug Abuse.
The clinical trial analyzed more than 51,000 hospitalizations. During the AI deployment period, 1.51% of patients received addiction consultations, compared to 1.35% without AI. Readmission rates also dropped to about 8% for AI-screened patients, compared to 14% in the provider-only group. Importantly, the results held steady even after adjusting for patient demographics and health conditions.
Lead author Dr. Majid Afshar noted the significance of integrating the AI tool into real-world hospital workflows. “Our study represents one of the first demonstrations of an AI screening tool embedded into addiction medicine,” he said.
Despite its success, researchers caution that challenges like alert fatigue and limited adoption across healthcare systems remain. Still, the results suggest AI could play a key role in addressing the nation’s opioid crisis, offering a scalable, cost-effective method for early intervention.
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