AI Screening for Opioid Use Disorder Associated with Fewer Hospital Readmissions
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An artificial intelligence (AI)-driven screening tool, developed by an NIH-funded research team, 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 with AI screening had 47% lower odds of being readmitted to the hospital within 30 days after their initial discharge.
The study, published in Nature Medicine, demonstrates AI’s potential to affect patient outcomes in real-world healthcare settings and suggests investment in AI may be a promising strategy.
In a clinical trial, researchers compared physician-led addiction specialist consultations to the performance of their AI screening tool. Researchers first measured the effectiveness of provider-led consultations. They then implemented the AI screening tool to assist the healthcare providers and remind them throughout hospitalization of a patient’s need for an addiction specialist’s care.
The AI screener analyzed information within all the documentation available in the electronic health records in real time, such as clinical notes and medical history, to identify features and patterns associated with opioid use disorder. Upon identification, the system issued an alert to providers when they opened the patient’s medical chart with a recommendation to order addiction medicine consultation and to monitor and treat withdrawal symptoms.
The trial found no decrease in quality with the AI-prompted consultation, which offered a more scalable and automated approach.
However, challenges remain, including potential alert fatigue among providers and the need for broader validation across different healthcare systems. Future research will focus on optimizing the AI tool’s integration and assessing its longer-term impact on patient outcomes.