NIH-supported clinical trial shows AI tools are as effective as healthcare providers in generating referrals to addiction specialists.
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 and 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 the hospital, compared to 14% in the traditional provider-led group.
The reduction in 30-day readmissions is 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 and 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.
This article was originally published here on April 3, 2025.
If you or someone you know is struggling or in crisis, help is available. Call or text 988 or chat at 988lifeline.org. To learn how to get support for mental health drug or alcohol conditions, visit FindSupport.gov. If you are ready to locate a treatment facility or provider, you can go directly to FindTreatment.gov or call 800-662-HELP (4357).
About the National Institute on Drug Abuse (NIDA): NIDA is a component of the National Institutes of Health, U.S. Department of Health and Human Services. NIDA supports most of the world’s research on the health aspects of drug use and addiction. The Institute carries out a large variety of programs to inform policy, improve practice, and advance addiction science. For more information about NIDA and its programs, visit www.nida.nih.gov.
About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and it investigates the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.
About substance use disorders: Substance use disorders are chronic, treatable conditions from which people can recover. In 2022, nearly 49 million people in the United States had at least one substance use disorder. Substance use disorders are defined in part by the continued use of substances despite negative consequences. They are also relapsing conditions, in which periods of abstinence (not using substances) can be followed by a return to use. Stigma can make individuals with substance use disorders less likely to seek treatment. Using preferred language can help accurately report on substance use and addiction. View NIDA’s online guide.
Reference
M Afshar, et al. Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults. Nature Medicine. DOI: 10.1038/s41591-025-03603-z
