Outcomes and Cost-Effectiveness of an EHR-Embedded AI Screener for Identifying Hospitalized Adults at Risk for Opioid Use Disorder

UNLABELLED: Hospitalized adults with opioid use disorder (OUD) are at high risk for adverse events and rehospitalizations. This pre-post quasi-experimental study evaluated whether an AI-driven OUD screener embedded in the electronic health record (EHR) was non-inferior to usual care in identifying patients for Addiction Medicine consults, aiming to provide a similarly effective but more scalable alternative to human-led ad hoc consultations. The AI screener analyzed EHR notes in real-time with a convolutional neural network to identify patients at risk and recommend consultation. The primary outcome was the proportion of patients receiving consults, comparing a 16-month pre-intervention period to an 8-month post-intervention period with the AI screener. Consults did not change between periods (1.35% vs 1.51%, p < 0.001 for non-inferiority). The AI screener was associated with a reduction in 30-day readmissions (OR: 0.53, 95% CI: 0.30-0.91, p = 0.02) with an incremental cost of $6,801 per readmission avoided, demonstrating its potential as a scalable, cost-effective solution for OUD care.

CLINICALTRIALSGOV ID: NCT05745480.