两种电子健康记录数据提取方法的比较,用于退伍军人老龄化队列指数研究中的实验室测试
Christine Ramsey-Hardy1,2,3, Melissa Skanderson2, Janet P Tate2,3
1Department of Nutritional Sciences, University of Texas at Austin, Austin, TX 78712, United States.
JAMIA open
|July 29, 2025
概括
从VA EHR中提取实验室数据的LOINC和VACS方法显示出高度一致. 推LOINC,因为它的效率和易用性,而不会牺牲有效性.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据管理 临床数据管理
- 电子健康记录 (EHR) 研究 研究
背景情况:
- 退伍军人健康管理局 (VA) 的电子健康记录 (EHR) 包含有价值的实验室化学数据.
- 标准化数据提取方法对于准确分析EHR数据至关重要.
- 通过比较不同的数据提取方法,可以确保数据的最佳利用.
研究的目的:
- 将观察医学结果伙伴关系 (OMOP) 逻辑观察标识符名称和代码 (LOINC) 方法与退伍军人衰老队列研究 (VACS) 方法进行比较.
- 评估这些方法在从VA EHR中提取实验室化学数据的有效性.
- 确定在老兵群体中进行实验室数据提取的最佳方法.
主要方法:
- 从VA EHR中提取了143,830名退伍军人的16项常见测试的实验室化学测试结果 (2015-2019).
- 采用了两个不同的数据提取方法:OMOP LOINC和VACS.
- 通过两种方法提取的数据的完整性和准确性进行了比较.
主要成果:
- 无论是LOINC还是VACS方法,都从130个VA站中捕获了高比例的实验室结果 (128-130个).
- 两种方法之间观察到很高的一致性,超过97%的观察结果对10个测试相匹配,其他测试超过92%.
- 测试结果值的准确匹配超过了10项测试的99%,所有其他测试的92%
结论:
- 两种LOINC和VACS方法都显示出高度一致性,并提取可比量的实验室数据.
- 由于LOINC方法的简单性和效率,它有利于提取实验室结果.
- 该LOINC方法提供有效的实验室数据提取,而不会影响准确性.
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