AI Referrals Can Reduce Opioid Addiction
A study suggests ways to increase access to treatment while saving money.
Jaime Espinoza
In a recent UW study, an AI screening tool successfully identified hospitalized adults at risk for opioid use disorder and recommended referral to inpatient addiction specialists.
The AI method was just as effective as a health provider in initiating addiction-specialist consultations and recommending monitoring of opioid withdrawal.
Compared to patients who received provider-initiated con- sultations, patients identified for addiction medicine referrals by AI screening had 47 percent lower odds of being readmitted to the hospital within 30 days after their initial discharge. This reduction in readmissions translated to a total of nearly $109,000 in estimated health care savings during the study period.
The study was led by Majid Afshar, an associate professor in the Department of Medicine at the UW’s School of Medicine and Public Health.
According to Afshar, the study suggests that investment in AI may be a promising strategy for health care systems seeking to increase access to addiction treatment while improving efficiencies and saving money.
“Our study represents one of the first demonstrations of an AI screening tool embedded into addiction medicine and hospital workflows, highlighting the pragmatism and real-world promise
Published in the Fall 2025 issue
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