Identifying Patients With High Data Completeness to Improve Validity of Comparative Effectiveness Research in Electronic Health Records Data

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.