Identifying Patients With High Data Completeness to Improve Validity of Comparative Effectiveness Research in Electronic Health Records Data
Identifying Patients With High Data Completeness to Improve Validity of Comparative Effectiveness Research in Electronic Health Records Data
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DOI：
10.1002/cpt.861
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发表时间：
2018-05-01
影响因子：
6.7
通讯作者：
Schneeweiss, Sebastian
中科院分区：
文献类型：
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作者：
Lin, Kueiyu Joshua;Singer, Daniel E.;Schneeweiss, Sebastian
电子健康记录（EHR）不连续，即研究的电子健康记录系统之外记录有医疗信息，在基于电子健康记录的比较有效性研究（CER）中与大量信息偏差有关。我们旨在开发并验证一个预测模型，以识别电子健康记录连续性高的患者，从而减少这种偏差。基于来自两个美国医疗服务提供者网络的183739名65岁患者的电子健康记录以及2007 - 2014年的医疗保险索赔数据，我们通过电子健康记录系统记录的就诊平均比例（MPEC）来量化电子健康记录连续性。我们使用一个电子健康记录系统作为训练集，另一个作为验证集构建了一个针对MPEC的预测模型。与其余研究人群相比，预测电子健康记录连续性处于前20%的患者，40个CER相关变量的错误分类要小3.5 - 5.8倍。根据预测的电子健康记录连续性，合并症情况没有显著差异。这些发现表明，将CER限制在预测电子健康记录连续性高的患者中，可能在有效性和普遍性之间取得有利的平衡。
Electronic health record (EHR)-discontinuity, i.e., having medical information recorded outside of the study EHR system, is associated with substantial information bias in EHR-based comparative effectiveness research (CER). We aimed to develop and validate a prediction model identifying patients with high EHR-continuity to reduce this bias. Based on 183,739 patients aged 65 in EHRs from two US provider networks linked with Medicare claims data from 2007-2014, we quantified EHR-continuity by mean proportion of encounters captured (MPEC) by the EHR system. We built a prediction model for MPEC using one EHR system as training and the other as the validation set. Patients with top 20% predicted EHR-continuity had 3.5-5.8-fold smaller misclassification of 40 CER-relevant variables, compared to the remaining study population. The comorbidity profiles did not differ substantially by predicted EHR-continuity. These findings suggest that restriction of CER to patients with high predicted EHR-continuity may confer a favorable validity to generalizability trade-off.