Department of Veterans Affairs, Cooperative Studies Program Palo Alto Coordinating Center, 701B North Shoreline Blvd, Mountain View, CA, 94043, USA.
Department of Medicine, Stanford University School of Medicine, 1265 Welch Road, Stanford, CA, 94305-5464, USA.
BMC Med Inform Decis Mak. 2021 Oct 20;21(1):289. doi: 10.1186/s12911-021-01643-2.
To describe an automated method for assessment of the plausibility of continuous variables collected in the electronic health record (EHR) data for real world evidence research use.
The most widely used approach in quality assessment (QA) for continuous variables is to detect the implausible numbers using prespecified thresholds. In augmentation to the thresholding method, we developed a score-based method that leverages the longitudinal characteristics of EHR data for detection of the observations inconsistent with the history of a patient. The method was applied to the height and weight data in the EHR from the Million Veteran Program Data from the Veteran's Healthcare Administration (VHA). A validation study was also conducted.
The receiver operating characteristic (ROC) metrics of the developed method outperforms the widely used thresholding method. It is also demonstrated that different quality assessment methods have a non-ignorable impact on the body mass index (BMI) classification calculated from height and weight data in the VHA's database.
The score-based method enables automated and scaled detection of the problematic data points in health care big data while allowing the investigators to select the high-quality data based on their need. Leveraging the longitudinal characteristics in EHR will significantly improve the QA performance.
描述一种自动化方法，用于评估电子健康记录（EHR）数据中用于真实世界证据研究的连续变量的合理性。
质量评估（QA）中最常用的连续变量方法是使用预设阈值检测不合理的数字。除了阈值方法之外，我们还开发了一种基于评分的方法，利用 EHR 数据的纵向特征来检测与患者病史不一致的观察值。该方法应用于退伍军人医疗保健管理局（VHA）百万退伍军人计划数据中的 EHR 中的身高和体重数据。还进行了一项验证研究。
开发的方法的接收者操作特征（ROC）指标优于广泛使用的阈值方法。还表明，不同的质量评估方法对 VHA 数据库中身高和体重数据计算的体重指数（BMI）分类有不可忽视的影响。
基于评分的方法能够在医疗保健大数据中自动、规模化地检测有问题的数据点，同时允许研究人员根据自己的需求选择高质量的数据。利用 EHR 的纵向特征将显著提高 QA 性能。
