Weiskopf Nicole G, Cohen Aaron M, Hannan Joely, Jarmon Thad, Dorr David A
Department of Medical Informatics and Clinical Epidemiology, OHSU, Portland, OR.
AMIA Annu Symp Proc. 2020 Mar 4;2019:903-912. eCollection 2019.
Structured electronic health record (EHR) data are often used for quality measurement and improvement, clinical research, and other secondary uses. These data, however, are known to suffer from quality problems. There may be value in augmenting structured EHR data to improve data quality, thereby improving the reliability and validity of the conclusions drawn from those data. Focusing on five diagnoses related to cardiovascular care, this paper considers the added value of two alternative data sources: manual chart abstraction and patient self-report. We assess the overall agreement between structured EHR problem list data, abstracted EHR data, and patient self- report; and explore possible causes of disagreement between those sources. Our findings suggest that both chart abstraction and patient self-report contain significantly more diagnoses than the problem list, but that the information they capture is different. Methods for collecting and validating self-reported medical data require further consideration and exploration.
结构化电子健康记录(EHR)数据常用于质量评估与改进、临床研究及其他二次利用。然而,这些数据存在质量问题。扩充结构化EHR数据以提高数据质量,进而提高从这些数据得出结论的可靠性和有效性,可能具有重要意义。本文聚焦于与心血管护理相关的五种诊断,探讨了两种替代数据源的附加价值:手工病历摘要和患者自我报告。我们评估了结构化EHR问题列表数据、提取的EHR数据和患者自我报告之间的总体一致性;并探究了这些数据源之间存在分歧的可能原因。我们的研究结果表明,病历摘要和患者自我报告包含的诊断都比问题列表显著更多,但它们所捕获的信息有所不同。收集和验证自我报告医疗数据的方法需要进一步思考和探索。