单臂试验临床试验协议:开发和评估机器学习阿片类药物预测和风险分层电子平台 (DEMONSTRATE)
Je-Won J Hong1, Debbie L Wilson2, Khoa Nguyen1
1Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA.
Journal of clinical medicine
|December 11, 2025
概括
本研究评估了一种机器学习工具,用于预测阿片类药物过量使用风险,旨在通过临床决策支持改善患者安全并减少伤害. 过量预防警报系统指导初级保健提供者管理高风险患者.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的机器学习
- 公共卫生干预 公共卫生干预
背景情况:
- 过量服用阿片类药物仍然是一个重大的公共卫生危机.
- 临床决策支持 (CDS) 工具可以帮助风险分层.
- DEMONSTRATE试验研究了一种新的机器学习 (ML) 方法来预测过量风险.
研究的目的:
- 评估基于ML的CDS工具 (过量预防警报) 的可用性,可接受性,可行性和有效性.
- 在三个月内识别高风险的阿片类药物过量患者.
- 为减少阿片类药物相关危害的战略提供信息.
主要方法:
- 一个单臂,在13个初级保健诊所进行前后实施研究.
- 混合方法评估,包括定量指标和定性访谈.
- 专注于年龄≥18岁的患者,最近的阿片类药物处方被ML算法确定为高风险.
主要成果:
- 通过6个有利的患者结局 (例如,纳洛接入,没有过量事件) 的组合来衡量有效性.
- 量化指标包括警报透率和采取的临床行动.
- 通过PCP问卷和采访评估可用性和可接受性.
结论:
- 该试验将为实施ML驱动的CDS工具提供现实世界的见解.
- 这些发现将指导未来的策略,以减轻与阿片类药物相关的危害.
- 这项研究支持将AI整合到针对阿片类药物安全的积极患者护理中.
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