AIによるオピオイド依存症スクリーニングが再入院率を低下(AI screening for opioid use disorder associated with fewer hospital readmissions)

2025-04-04

2025-04-03 アメリカ国立衛生研究所(NIH)

米国国立衛生研究所(NIH)によると、人工知能(AI)を活用したスクリーニングツールが、入院中の成人患者におけるオピオイド使用障害(OUD)のリスクを効果的に特定し、依存症専門医への紹介を促進することが示されました。このAIベースの手法は、医療提供者が主導する従来の方法と同等の効果を持ち、オピオイド離脱のモニタリングを推奨する点でも同様の成果を上げました。さらに、AIスクリーニングを受けた患者は、従来の方法でケアを受けた患者に比べ、退院後30日以内の再入院率が47%低下し、これにより試験期間中に約10万9,000ドルの医療費削減が達成されました。

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入院中の成人におけるオピオイド使用障害リスクに対するAIベースのスクリーニングの臨床的実施 Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults

Majid Afshar,Felice Resnik,Cara Joyce,Madeline Oguss,Dmitriy Dligach,Elizabeth S. Burnside,Anne Gravel Sullivan,Matthew M. Churpek,Brian W. Patterson,Elizabeth Salisbury-Afshar,Frank J. Liao,Cherodeep Goswami,Randy Brown & Marlon P. Mundt
Nature MedicinePublished:03 April 2025
DOI:https://doi.org/10.1038/s41591-025-03603-z

Abstract

Adults with opioid use disorder (OUD) are at increased risk for opioid-related complications and repeated hospital admissions. Routine screening for patients at risk for an OUD to prevent complications is not standard practice in many hospitals, leading to missed opportunities for intervention. The adoption of electronic health records (EHRs) and advancements in artificial intelligence (AI) offer a scalable approach to systematically identify at-risk patients for evidence-based care. This pre–post quasi-experimental study evaluated whether an AI-driven OUD screener embedded in the EHR was non-inferior to usual care in identifying patients for addiction medicine consultations, aiming to provide a similarly effective but more scalable alternative to human-led ad hoc consultations. The AI screener used a convolutional neural network to analyze EHR notes in real time, identifying patients at risk and recommending consultations. The primary outcome was the proportion of patients who completed a consultation with an addiction medicine specialist, which included interventions such as outpatient treatment referral, management of complicated withdrawal, medication management for OUD and harm reduction services. The study period consisted of a 16-month pre-intervention phase followed by an 8-month post-intervention phase, during which the AI screener was implemented to support hospital providers in identifying patients for consultation. Consultations did not change between periods (1.35% versus 1.51%, P < 0.001 for non-inferiority). In secondary outcome analysis, the AI screener was associated with a reduction in 30-day readmissions (odds ratio: 0.53, 95% confidence interval: 0.30–0.91, P = 0.02) with an incremental cost of US$6,801 per readmission avoided, demonstrating its potential as a scalable, cost-effective solution for OUD care. ClinicalTrials.gov registration: NCT05745480.