https://orcid.org/0000-0002-9781-0477
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Abstract
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.
Disclosure
Dr Robert Glynn reports grants from Kowa, Novartis, Pfizer, and Astra Zeneca, outside the submitted work; Dr Sebastian Schneeweise reports personal fees from WHISCON, LLC, Aetion, Inc., outside the submitted work. The authors report no other conflicts of interest in this work.