It is unclear how out-of-system care or electronic health record (EHR) discontinuity (i.e., receiving care outside of an EHR system) may affect validity of comparative effectiveness research using these data. We aimed to compare the misclassification of key variables in patients with high versus low EHR continuity.
The study cohort comprised patients ages ≥65 identified in electronic health records from two US provider networks linked with Medicare insurance claims data from 2007 to 2014. By comparing electronic health records and claims data, we quantified EHR continuity by the proportion of encounters captured by the EHRs (i.e., “capture proportion”). Within levels of EHR continuity, for 40 key variables, we quantified misclassification by mean standardized differences between coding based on EHRs alone versus linked claims and EHR data.
Based on 183,739 patients, we found that mean capture proportion in a single electronic health record system was 16%–27% across two provider networks. Patients with highest level of EHR continuity (capture proportion ≥ 80%) had 11.4- to 17.4-fold less variable misclassification, when compared with those with lowest level of EHR continuity (capture proportion< 10%). Capturing at least 60% of the encounters in an EHR system was required to have reasonable variable classification (mean standardized difference <0.1). We found modest differences in comorbidity profiles between patients with high and low EHR continuity.
EHR discontinuity may lead to substantial misclassification in key variables. Restricting comparative effectiveness research to patients with high EHR continuity may confer a favorable benefit (reducing information bias) to risk (losing generalizability) ratio.
From the aDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; bDepartment of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA; cDepartment of Epidemiology, Harvard School of Public Health, Boston, MA; and dDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Submitted June 1, 2017; accepted December 21, 2017.
Data for replication: Data used to generate the study findings are not available due to restriction of the data use agreement with Centers for Medicare & Medicaid Services (CMS). The detailed definition of all variables in the study can be found in the supplemental materials (eAppendix 1; http://links.lww.com/EDE/B303).
This project was supported by NIH Grant 1R01LM012594-01. K.J.L. received a stipend from the Pharmacoepidemiology program in the Department of Epidemiology, Harvard School of Public Health and Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School. D.E.S. was supported by the Eliot B. and Edith C. Shoolman Fund of Massachusetts General Hospital. S.S. and S.N.M. were supported by PCORI grant 282364.5077585.0007 (ARCH/SCILHS).
The authors report no conflicts of interest.
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Correspondence: Kueiyu Joshua Lin, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA 02120. E-mail: firstname.lastname@example.org.