在电子病历中自动生成对照患者
Joseph Rigdon1,2,3, Brian Ostasiewski2,3, Kamah Woelfel3
1Department of Biostatistics and Data Science Wake Forest University School of Medicine Winston-Salem North Carolina USA.
Learning health systems
|January 22, 2024
概括
从电子医疗记录 (EMR) 中生成比较组是可行的,用于比较有效性研究. 这种方法有效地识别了观察性研究的患者比较组,增强了学术学习健康系统 (aLHS) 模型.
科学领域:
- 医疗信息学 医疗信息学
- 相对有效性研究研究比较
- 临床数据科学 临床数据科学
背景情况:
- 随机试验是理想的,但并不总是可行的或道德的.
- 电子医疗记录 (EMR) 为比较有效性研究提供了一个平台.
- 一个关键的挑战是有效地从EMR数据生成患者比较组.
研究的目的:
- 测试在EMR系统中选择患者对照者的多步骤过程.
- 评估EMR衍生的比较器生成的可行性和性能.
主要方法:
- 在一个学术学习健康系统 (aLHS) 中进行了一项混合方法研究.
- 研究人员调查了270名个人,以确定匹配的重要变量.
- 临床专家对变量进行了排名,并对EMR数据应用了贪匹配技术.
主要成果:
- 为匹配选择了9个变量,包括人口统计,并发症和社会经济因素.
- 该过程有效地为180万名患者生成了100个匹配的比较器.
- 该方法证明了可行性,速度 (不到100分钟) 和准确性 (欧几里德距离0.043).
结论:
- 由EMR衍生的匹配是一种可行和可重复的方法,用于创建比较数据.
- 这种方法支持观察性研究和学术学习健康系统 (aLHS) 模型.
- 建议在临床研究应用中进行进一步测试.
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