Kueiyu Joshua Lin

Objective: Electronic health records (EHR) data-discontinuity, i.e. receiving care outside of a particular EHR system, may cause misclassification of study variables. We aimed to validate an algorithm to identify patients with high EHR data-continuity to reduce such bias. Materials and Methods: We analyzed data from two EHR systems linked with Medicare claims data from 2007 through 2014, one in Massachusetts (MA, n=80,588) and the other in North Carolina (NC, n=33,207). We quantified EHR data-continuity by Mean Proportion of Encounters Captured (MPEC) by the EHR system when compared to complete recording in claims data. The prediction model for MPEC was developed in MA and validated in NC. Stratified by predicted EHR data-continuity, we quantified misclassification of 40 key variables by Mean Standardized Differences (MSD) between the proportions of these variables based on EHR alone vs the linked claims-EHR data. Results: The mean MPEC was 27% in the MA and 26% in the NC system. The predicted and observed EHR data-continuity was highly correlated (Spearman correlation=0.78 and 0.73, respectively). The misclassification (MSD) of 40 variables in patients of the predicted EHR data-continuity cohort was significantly smaller (44%, 95% CI: 40– 48%) than that in the remaining population. Discussion: The comorbidity profiles were similar in patients with high vs low EHR data-continuity. Therefore, restricting an analysis to patients with high EHR data-continuity may reduce information bias while preserving the representativeness of the study cohort. Conclusion: We have successfully validated an algorithm that can identify a high EHR data-continuity cohort representative of the source population.

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

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.

Health insurance claims and electronic health records (EHR) databases have been considered the preferred data sources with which to study drug safety and effectiveness in routine care. Linking claims data to EHR allows researchers to leverage the complementary advantages of each data source to enhance study validity. We propose a framework to evaluate the need for supplementing claims data with EHR and vice versa to optimize outcome ascertainment, exposure assessment, and confounding adjustment.