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Identifying Patients With High Data Completeness to Improve Validity of Comparative Effectiveness Research in
Kueiyu Joshua Lin1,2,3, Daniel E Singer2,3, Robert J Glynn1,3
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Abstract:
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.
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