An artificial intelligence (AI)-driven screening tool, developed by a National Institutes of Health (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. This reduction in readmissions translated to a total of nearly $109,000 in estimated healthcare savings during the study period.
The study, published in Nature Medicine, reports the results of a completed clinical trial, demonstrating AI's potential to affect patient outcomes in real-world healthcare settings. The study suggests investment in AI may be a promising strategy specifically for healthcare systems seeking to increase access to addiction treatment while improving efficiencies and saving costs.
"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 Nora D. Volkow, M.D., 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."
In a clinical trial , researchers at the University of Wisconsin School of Medicine and Public Health, Madison, compared physician-led addiction specialist consultations to the performance of their AI screening tool, which had been developed and validated in prior work. Researchers first measured the effectiveness of provider-led consultations at the University Hospital in Madison, Wisconsin, between March to October 2021 and March to October 2022, whereby healthcare providers conducted ad hoc addiction specialist consultations for opioid use disorder. They then implemented the AI screening tool between March to October 2023 to assist the healthcare providers and remind them throughout hospitalization of a patient's need for an addiction specialist's care. From start to finish, the trial screened 51,760 adult hospitalizations, with 66% occurring without deploying the AI screener and 34% with the AI screener deployed hospital-wide. A total of 727 addiction medicine consultations were completed during the study period.
The AI screener was built to recognize patterns in data, like how our brains process visual information. It 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 that AI-prompted consultation was just as effective as provider-initiated consultation, ensuring no decrease in quality while offering a more scalable and automated approach. Specifically, the study showed that 1.51% of hospitalized adults received an addiction medicine consultation when healthcare professionals used the AI screening tool, compared to 1.35% without the assistance of the AI tool. Additionally, the AI screener was associated with fewer 30-day readmissions, with approximately 8% of hospitalized adults in the AI screening group being readmitted to hospital, compared to 14% in the traditional provider-led group.
The reduction in 30-day readmissions still held after accounting for patients' age, sex, race and ethnicity, insurance status, and comorbidities, as calculated via an odds ratio. When analyzing the results using the odds ratio, the researchers estimated a decrease of 16 readmissions by employing the AI screener. A subsequent cost-effectiveness analysis indicated a net cost of $6,801 per readmission avoided for the patient, healthcare insurer, and/or the hospital. This amounted to an estimated total of $108,800 in healthcare savings for the eight-month study period in which the AI screener was used, even after accounting for the costs of maintaining the AI software. The average cost of a 30-day hospital readmission is currently estimated at $16,300.
"AI holds promise in medical settings, but many AI-based screening models have remained in the development phase, without integration into real-world settings," said Majid Afshar, M.D., lead author of the study and associate professor at the University of Wisconsin-Madison. "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."
While the AI screener showed strong effectiveness, challenges remain, including potential alert fatigue among providers and the need for broader validation across different healthcare systems. The authors also note 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 opioid crisis continues to strain healthcare systems in the U.S., with emergency department admissions for substance use increasing by nearly 6% between 2022 to 2023 to an estimated 7.6 million . Opioids are the second leading cause of these visits after alcohol, but screening for opioid use disorder in hospitals remains inconsistent. As a result, hospitalized patients with opioid use disorder frequently leave the hospital before seeing an addiction specialist, a factor linked to a tenfold increase in overdose rates. AI technology has emerged as a novel, scalable tool to potentially overcome these barriers and improve opportunities for early intervention and linkage to medications for opioid use disorder , but more research is needed to understand how AI can be used effectively in healthcare settings.
