Opportunities, Pitfalls, and Alternatives in Adapting Electronic Health Records for Health Services Research.
Opportunities, Pitfalls, and Alternatives in Adapting Electronic Health Records for Health Services Research.
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机会，陷阱，和选择适应电子健康记录为卫生服务研究。
DOI：
10.1177/0272989x20954403
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Einstadter D
中科院分区：
文献类型：
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作者：
Taksler GB;Dalton JE;Perzynski AT;Rothberg MB;Milinovich A;Krieger NI;Dawson NV;Roach MJ;Lewis MD;Einstadter D
电子健康记录（EHR）提供了研究大量患者的潜力，但它是为临床实践而设计的，而不是研究。尽管可用性越来越高，但利用EHR数据进行研究也面临着一系列挑战。在本文中，我们描述了一些重要的考虑因素和潜在的解决方案时，经常遇到的问题，大规模的，EHR派生的数据，卫生服务和社区相关的健康研究。具体而言，使用EHR数据需要研究人员定义相关的患者亚群，可靠地识别初级保健提供者，识别EHR包含偶发事件（即，非结构化纵向）数据，说明卫生系统组成和治疗方案随时间的变化，了解EHR并不总是组织良好和准确的，设计方法来识别多个卫生系统中的同一患者，说明EHR的巨大规模，并考虑数据访问的障碍。在EHR中发现的关联可能不代表一般人群中的关联，但对基于EHR的关联的清晰理解对于改善患者学习医疗保健系统的结果的过程可能非常有价值。在建立两个大规模的EHR衍生数据集的卫生服务研究的背景下，我们描述了EHR数据的潜在陷阱，并提出了一些解决方案，为那些计划使用EHR数据在他们的研究。随着越来越多的临床数据积累在EHR中，使用这些数据进行研究将变得越来越普遍和重要。注意EHR数据的复杂性，将允许更明智的分析和解释的结果，从EHR为基础的数据集。
Electronic health records (EHRs) offer potential to study large numbers of patients but are designed for clinical practice, not research. Despite increasing availability, utilizing EHR data for research comes with its own set of challenges. In this paper, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be non-representative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building two large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.