制定临床决策支持框架,将预测模型整合到家庭医疗保健中的常规护理实践中,为心力衰竭患者提供医疗保健服务
Sena Chae1, Anahita Davoudi2, Jiyoun Song3
1College of Nursing, The University of Iowa, Iowa City, Iowa, USA.
概括
家庭医疗保健 (HHC) 临床医生可以使用新的AI驱动的临床决策支持 (CDS) 工具来预防心力衰竭 (HF) 住院. 该工具通过分析患者数据来预测不良事件,从而实现及时干预.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 医疗保健行业优先考虑个性化护理,但在家庭医疗保健 (HHC) 中的心力衰竭 (HF) 患者经常面临住院治疗.
- 基于风险预测的及时干预对于HHC临床医生来说至关重要,以防止急诊室 (ED) 访问和住院.
研究的目的:
- 为了确定预测ED访问和住院的关键变量,在接受HC的HF患者中HHC.
- 将临床决策支持 (CDS) 工具的数据要求映射到可交换的数据标准,用于整合HF患者护理.
- 概述一个开发实时,基于人工智能 (AI) 的CDS工具的管道.
主要方法:
- 利用来自美国东北部一个大型HHC组织 (9,362名患者,12,223次护理事件) 的患者数据.
- 检查了生命体征,HHC访问细节和临床笔记衍生变量,以确定ED访问和住院的预测因素.
- 将临床人口统计资料映射到可交换的数据标准中;由于其非结构化数据来源,NLP驱动的变量无法映射.
主要成果:
- 自HC入院以来的时间和最近的HF症状存在显著预测了不良事件.
- 在急救诊所访问或住院之前的最后几次HHC访问的数据特别具有预测性.
- 开发的CDS工具提醒护士新出现的风险,促进知情决策.
结论:
- 一个时间序列风险预测模型及其在HHC中的CDS应用可以提高HF患者的患者结果和资源利用率.
- 拟议的基于人工智能的CDS工具与预警系统作为防范ED访问和住院的保障,改善护理决策和患者的结果.
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