电子病历中的测量误差和错误分类：减轻偏差的方法。
Measurement error and misclassification in electronic medical records: methods to mitigate bias.
作者信息
Young Jessica C, Conover Mitchell M, Funk Michele Jonsson
机构信息
Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
出版信息
Curr Epidemiol Rep. 2018 Dec;5(4):343-356. doi: 10.1007/s40471-018-0164-x. Epub 2018 Sep 10.
PURPOSE OF REVIEW
We sought to: 1) examine common sources of measurement error in research using data from electronic medical records (EMR), 2) discuss methods to assess the extent and type of measurement error, and 3) describe recent developments in methods to address this source of bias.
RECENT FINDINGS
We identified eight sources of measurement error frequently encountered in EMR studies, the most prominent being that EMR data usually reflect only the health services and medications delivered within the specific health facility/system contributing to the EMR data. Methods for assessing measurement error in EMR data usually require gold standard or validation data, which may be possible using data linkage. Recent methodological developments to address the impact of measurement error in EMR analyses were particularly rich in the multiple imputation literature.
SUMMARY
Presently, sources of measurement error impacting EMR studies are still being elucidated, as are methods for assessing and addressing them. Given the magnitude of measurement error that has been reported, investigators are urged to carefully evaluate and rigorously address this potential source of bias in studies based in EMR data.
综述目的
我们试图：1）使用电子病历（EMR）数据检查研究中测量误差的常见来源；2）讨论评估测量误差程度和类型的方法；3）描述解决这种偏差来源的方法的最新进展。
最新发现
我们确定了EMR研究中经常遇到的八个测量误差来源，最突出的是EMR数据通常仅反映特定卫生机构/系统内提供的医疗服务和药物，而这些数据构成了EMR数据。评估EMR数据测量误差的方法通常需要金标准或验证数据，使用数据链接可能可以实现。近期解决EMR分析中测量误差影响的方法学进展在多重插补文献中尤为丰富。
总结
目前，影响EMR研究的测量误差来源仍在阐明中，评估和解决这些误差的方法也是如此。鉴于已报告的测量误差程度，敦促研究人员在基于EMR数据的研究中仔细评估并严格解决这一潜在的偏差来源。
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