AI screening for opioid use disorder associated with fewer hospital readmissions

Artificial Intelligence in Identifying and Managing Opioid Use Disorder

An artificial intelligence (AI)-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 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 health care savings during the study period.

Study Details and Findings

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 health care settings. The study suggests investment in AI may be a promising strategy specifically for health care systems seeking to increase access to addiction treatment while improving efficiencies and saving 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.

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.

Effectiveness and Cost Savings

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 health care 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 subsequent cost-effectiveness analysis indicated a net cost of $6,801 per readmission avoided for the patient, health care insurer, and/or the hospital. This amounted to an estimated total of $108,800 in health care 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.

Future Implications and Challenges

While the AI screener showed strong effectiveness, challenges remain, including potential alert fatigue among providers and the need for broader validation across different health care systems. 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 health care systems in the U.S., with emergency department admissions for substance use increasing. 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 health care settings.