休斯顿卫理心血管学习健康系统 (CVD-LHS) 登记:使用信息学框架方法开发和实施基于电子医疗记录的自动化登记系统的方法
Khurram Nasir1,2, Rakesh Gullapelli2, Juan C Nicolas2
1Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States.
American journal of preventive cardiology
|May 17, 2024
概括
一个新的注册系统有效地识别了动脉样硬化心血管疾病 (ASCVD) 患者和风险患者,使用自动数据提取. 这通过创建一个全面的,数据驱动的患者数据库来促进人口健康管理和心血管研究.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 人口健康管理 人口健康管理
背景情况:
- 动脉样硬化心血管疾病 (ASCVD) 是一个重大的公共卫生挑战.
- 有效的管理需要系统地识别和跟踪风险和已建立的患者群体.
- 目前用于患者识别的方法可以是人工和劳动密集型的.
研究的目的:
- 在一个大型医疗保健系统中建立一个全系统的风险和已确定的ASCVD患者登记册.
- 利用自动数据提取来识别患者负担,决定因素和风险范围.
- 通过数据驱动的洞察力,为人口健康管理提供信息,并推进心血管研究和护理.
主要方法:
- 成年门诊患者的回顾性多中心队列分析 (2016年6月 - 2022年12月).
- 基于电子医疗记录 (EMR) 的注册表的开发,使用自动数据提取的共同框架.
- 从外部来源将临床数据与健康的社会决定因素集成;利用SQL Server管理工作室进行数据处理.
主要成果:
- 成功开发了一个基于EMR的实时,无标识,自动更新的注册表 (休斯顿卫理会心血管疾病学习健康系统 - HM CVD-LHS).
- 注册表包含大约450个变量,包括人口统计,诊断,实验室,药物和并发症.
- 在1,171,768名成年人中确定了113,022名 (9.6%) 的ASCVD患者,并对患者子组进行了详细分析.
结论:
- 在HM CVD-LHS注册成功列出了已确定的ASCVD患者和那些处于风险的患者.
- 从EMR中自动提取数据为手动图表抽象提供了一个可行的替代方案.
- 该框架支持知识推断和建立专门的患者登记册.
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