A Framework for Visualizing Study Designs and Data Observability in Electronic Health Record Data.
A Framework for Visualizing Study Designs and Data Observability in Electronic Health Record Data.
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DOI：
10.2147/clep.s358583
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发表时间：
2022
影响因子：
3.9
通讯作者：
中科院分区：
文献类型：
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人们对使用临床实践数据产生的证据来支持监管、覆盖和其他医疗保健决策越来越感兴趣。一种描述纵向研究设计的图形框架被引入，以减轻这一障碍，并已被广泛接受。我们试图增强框架，以包含有助于读者评估应用研究设计的源数据的适当性的信息。对于增强的图形框架，我们添加了数据类型和可观察性的简单可视化，以捕获电子健康记录（EHR）和其他可能具有有限数据连续性的注册中心数据以及具有注册文件的保险索赔数据之间的差异。我们通过使用不同数据源进行的2个示例研究来说明修订后的图形框架，包括仅行政索赔，仅电子病历，关联索赔和电子病历，以及基于专业社区的电子病历，有和没有外部链接。增强的可视化框架很重要，因为研究效度评估需要同时考虑研究问题、设计和数据这三个因素。任何给定的数据来源或研究设计可能适用于某些问题，但不适用于其他问题。
There is growing interest in using evidence generated from clinical practice data to support regulatory, coverage and other healthcare decision-making. A graphical framework for depicting longitudinal study designs to mitigate this barrier was introduced and has found wide acceptance. We sought to enhance the framework to contain information that helps readers assess the appropriateness of the source data in which the study design was applied. For the enhanced graphical framework, we added a simple visualization of data type and observability to capture differences between electronic health record (EHR) and other registry data that may have limited data continuity and insurance claims data that have enrollment files. We illustrate the revised graphical framework with 2 example studies conducted using different data sources, including administrative claims only, EHR only, linked claims and EHR, as well as specialty community based EHRs with and without external linkages. The enhanced visualization framework is important because evaluation of study validity needs to consider the triad of study question, design, and data together. Any given data source or study design may be appropriate for some questions but not others.