使用机器学习的质量改进研究 机器学习的死亡风险预测模型 关于高风险患者预先护理计划的通知系统
Jonathan Walter1, Jessica Ma1,2, Alyssa Platt3
1Department of Medicine Duke University School of Medicine.
Journal of Brown hospital medicine
|March 3, 2025
概括
机器学习 (ML) 模型可以识别患者进行预先护理规划 (ACP). 提供者通知显著增加了住院患者的ACP记录率,改善了护理协调.
科学领域:
- 医疗信息学 医疗信息学
- 改善医疗保健质量 改善医疗保健质量
- 临床决策支持 临床决策支持
背景情况:
- 提前护理规划 (ACP) 是至关重要的,但在患者护理中未得到充分利用.
- 机器学习 (ML) 提供了识别需要ACP的患者的潜力.
- 目前用于识别ACP患者的方法是不理想的.
研究的目的:
- 评估 ML 驱动的提供者通知对 ACP 文件化率的影响.
- 评估ML识别的患者警报对ACP完成的影响.
- 确定基于ML的干预措施是否改善了与ACP相关的患者结果.
主要方法:
- 在一所高等学术医院进行了一项前后质量改善 (QI) 研究.
- 通过ML模型确定患有高死亡风险的成人通用医学患者被纳入.
- 这项干预涉及向提供者发送电子邮件和呼叫器通知,以确定患者的身份.
主要成果:
- 与共变量调整后的ACP文件从干预后的6.0%上升到56.5% (RR=9.42).
- 患有ACP的患者更有可能减少代码状态 (RR=2.69) 和增加住院转诊 (OR=2.16).
- 在经过记录的ACP (1.29事件时间比率) 的患者中观察到更长的平均停留时间 (LOS).
结论:
- 使用ML模型的提供者通知有效地增加了ACP在住院患者环境中的文档.
- ML驱动的警报可以提高临床医生的参与,提前进行护理规划.
- 这种方法有望改善终身护理讨论和文档.
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