Afshar Majid, Resnik Felice, Joyce Cara, Oguss Madeline, Dligach Dmitriy, Burnside Elizabeth, Sullivan Anne, Churpek Matthew, Patterson Brian, Salisbury-Afshar Elizabeth, Liao Frank, Brown Randall, Mundt Marlon
University of Wisconsin - Madison.
Loyola University Chicago Stritch School of Medicine.
Res Sq. 2024 Oct 14:rs.3.rs-5200964. doi: 10.21203/rs.3.rs-5200964/v1.
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.
NCT05745480.
患有阿片类物质使用障碍（OUD）的住院成年人发生不良事件和再次住院的风险很高。这项前后对照的准实验研究评估了嵌入电子健康记录（EHR）中的人工智能驱动的OUD筛查工具在识别需要成瘾医学咨询的患者方面是否不劣于常规护理，旨在提供一种同样有效但更具可扩展性的替代方案，以取代人工主导的临时咨询。人工智能筛查工具使用卷积神经网络实时分析EHR记录，以识别有风险的患者并推荐咨询。主要结局是接受咨询的患者比例，比较了使用人工智能筛查工具的16个月干预前期和8个月干预后期。两个时期之间的咨询情况没有变化（1.35%对1.51%，非劣效性p<0.001）。人工智能筛查工具与30天再入院率的降低相关（比值比：0.53，95%置信区间：0.30 - 0.91，p = 0.02），避免每次再入院的增量成本为6801美元，证明了其作为OUD护理的可扩展、具有成本效益的解决方案的潜力。
NCT05745480。
