为熟练护理机构患者开发预测性住院模型
Ben Kandel1, Cheryl Field1, Jasmeet Kaur1
1PointClickCare Technologies Inc., Mississauga, ON, Canada.
Journal of the American Medical Directors Association
|September 30, 2024
概括
一种新的机器学习模型可以在7天内预测高风险住院或死亡的熟练护理机构 (SNF) 患者. 这种工具有助于SNF在主动监测和再接收减少计划中.
科学领域:
- 医疗保健分析 医疗保健分析
- 临床信息学 临床信息学
- 预测建模预测建模
背景情况:
- 熟练护理机构 (SNFs) 在识别住院或死亡高风险的患者方面面临挑战.
- 准确的风险预测对于质量测量,财政处罚和有效的临床人员配置至关重要.
研究的目的:
- 开发和验证一种预测模型,用于识别可能在7天内住院或死亡的SNF患者.
- 将模型的表现与临床医生的判断进行比较.
主要方法:
- 使用电子健康记录 (EHR) 数据,在8440个美国SNF中开发了5642474名患者的回顾性多变量预后模型.
- 一个机器学习模型被训练了生命体征,诊断,实验室结果,食物摄入量和临床笔记.
- 模型的性能与SNF护士和医院病例经理的预测进行了比较.
主要成果:
- 开发的机器学习模型实现了0.75.5的接收器操作员曲线 (AUC) 下的面积.
- 该模型的灵敏度为35%,特异性为92%.
- 与模型相比,临床判断显示了更高的敏感性 (61%),但较低的特异性 (73%) 和积极的预测值 (10%).
结论:
- 机器学习模型可以准确预测SNF患者的7天住院或死亡风险.
- 这些模型为减少再接收计划提供了有价值的工具,而不会增加SNF员工的工作量.
- 这些预测工具可以促进对高风险患者的积极,有针对性的监测.
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