A clinical trial supported by the National Institutes of Health (NIH) has demonstrated that an artificial intelligence (AI) screening tool for opioid use disorder successfully reduced hospital readmissions by 47% compared to traditional provider-initiated consultations. The study, published in Nature Medicine, represents a significant advancement in applying AI technology to address the ongoing opioid crisis in the United States.
The AI system, developed by researchers at the University of Wisconsin School of Medicine and Public Health, was designed to identify hospitalized adults at risk for opioid use disorder and recommend referrals to inpatient addiction specialists. The trial screened 51,760 adult hospitalizations across multiple study periods at the University Hospital in Madison, Wisconsin.
AI Screening Shows Comparable Effectiveness with Added Benefits
The research team found that the AI-based method was equally effective as the health provider-only approach in initiating addiction specialist consultations and recommending monitoring of opioid withdrawal. Specifically, 1.51% of hospitalized adults received an addiction medicine consultation when healthcare professionals used the AI screening tool, compared to 1.35% without AI assistance.
"Addiction care remains heavily underprioritized and can be easily overlooked, especially in overwhelmed hospital settings where it can be challenging to incorporate resource-intensive procedures such as screening," said Dr. Nora D. Volkow, director of NIH's National Institute on Drug Abuse (NIDA). "AI has the potential to strengthen implementation of addiction treatment while optimizing hospital workflow and reducing healthcare costs."
The most striking finding was the significant reduction in 30-day hospital readmissions. Patients screened using the AI tool had approximately 8% readmission rates, compared to 14% in the traditional provider-led group. This 47% reduction in readmission odds persisted even after accounting for patients' age, sex, race and ethnicity, insurance status, and comorbidities.
Real-World Implementation and Cost Savings
The AI screening tool was integrated into the hospital's electronic health record system, analyzing clinical notes and medical history in real-time to identify patterns associated with opioid use disorder. When the system identified at-risk patients, it issued alerts to providers with recommendations for addiction medicine consultation and withdrawal symptom monitoring.
Dr. Majid Afshar, lead author of the study and associate professor at the University of Wisconsin-Madison, emphasized the practical significance of the implementation: "AI holds promise in medical settings, but many AI-based screening models have remained in the development phase, without integration into real-world settings. 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."
The reduction in readmissions translated to substantial cost savings. A cost-effectiveness analysis indicated a net cost of $6,801 per readmission avoided, amounting to an estimated total of $108,800 in healthcare savings during the eight-month study period when the AI screener was deployed. These savings were calculated after accounting for the costs of maintaining the AI software, with the average cost of a 30-day hospital readmission currently estimated at $16,300.
Addressing the Opioid Crisis Through Technology
The implementation of this AI tool comes at a critical time in the ongoing opioid crisis. Emergency department admissions for substance use increased by nearly 6% between 2022 and 2023, reaching an estimated 7.6 million visits. Opioids are the second leading cause of these visits after alcohol.
Current screening for opioid use disorder in hospitals remains inconsistent, with many patients leaving before seeing an addiction specialist—a factor linked to a tenfold increase in overdose rates. The AI technology offers a scalable solution to overcome these barriers and improve opportunities for early intervention.
Challenges and Future Directions
While the AI screener showed strong effectiveness, researchers acknowledged several challenges that need to be addressed. These include potential alert fatigue among providers and the need for broader validation across different healthcare systems.
The authors also noted that while the various study periods—spanning multiple years—were seasonally matched, the evolving nature of the opioid crisis may have introduced residual biases. Future research will focus on optimizing the AI tool's integration and assessing its longer-term impact on patient outcomes.
The study methodology involved comparing physician-led addiction specialist consultations to the performance of the AI screening tool across different time periods. Researchers first measured the effectiveness of provider-led consultations between March to October 2021 and March to October 2022, then implemented the AI screening tool between March to October 2023. From start to finish, 66% of screenings occurred without deploying the AI screener and 34% with the AI screener deployed hospital-wide, with a total of 727 addiction medicine consultations completed during the study period.
This research demonstrates AI's potential to affect patient outcomes in real-world healthcare settings and 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.
