Study: AI screening tool helps refer patients for opioid use disorder treatment

In a study led by Majid Afshar, MD, MS, associate professor, Division of Allergy, Pulmonary and Critical Care Medicine, an artificial intelligence-driven screening tool successfully identified hospitalized adults at risk for opioid use disorder and recommended referral to inpatient addiction specialists.

The AI-based method was just as effective as a health provider-only approach in initiating addiction specialist consultations and recommending monitoring of opioid withdrawal.

Compared to patients who received provider-initiated consultations, patients identified for addiction medicine referrals by AI screening and who received consultations had 47% 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, which was recently published in Nature Medicine, reports the results of a completed National Institutes of Health-funded clinical trial, demonstrating AI’s potential to affect patient outcomes in real-world healthcare settings.

According to Dr. Afshar, the study suggests that investment in AI may be a promising strategy for healthcare systems seeking to increase access to addiction treatment while improving efficiencies and saving costs.

“AI holds promise in medical settings, but many AI-based screening models have remained in the development phase, without integration into real-world settings,” he said.

“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 of this approach.”

Read the full story at the School of Medicine and Public Health

Banner: Majid Afshar, MD, MS. Credit: Clint Thayer/Department of Medicine.