Assessing the quality of electronic health record data and the claims linked data for target trial emulation studies

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Objectives: To evaluate whether an electronic health record (EHR) cohort has sufficient data quality to support target trial emulation design elements, using Medicare claims as a reference for billing-derived domains and linked EHR-claims data to illustrate the incremental value of linkage. Materials and Methods: We constructed annual University of Florida Health EHR-Medicare linked cohorts of patients ≥65 years with type 2 diabetes (T2D) from 2013 to 2020. Using Medicare claims as the reference, we assessed EHR data quality for target trial emulation-relevant elements across completeness, accuracy, plausibility, and concordance, spanning eligibility, exposure/new-user ascertainment, baseline covariates, outcomes, and follow-up. We also descriptively compared capture of target trial emulation elements across EHR-only, claims-only, and linked data. Results: The mean annual EHR-Medicare linked cohort included 12 895 patients (mean age 74.9 years; 58.0% female). Demographics were complete and highly accurate. In the EHR-only cohort, completeness ranged 34.1%-78.4% for conditions and 53.7%-63.4% for glucose-lowering drugs (GLDs). Accuracy was high for prevalent conditions and GLD use but low for incident measures. Plausible values were common (>98.5%), and hemoglobin A1c-T2D concordance was strong (98.6%). Linking EHR and claims substantially improved completeness and accuracy, especially for encounters, mortality, incident diagnoses, and medications. Discussion: The linked dataset addressed major limitations of EHR-only data and provided enhanced granularity compared to claims alone, offering a comprehensive resource for real-world target trial emulation research. Conclusion: Electronic health records offer valuable clinical details but face data quality challenges. Robust quality assurance strategies and linkage with external data are essential to strengthen real-world evidence and support target trial emulation.
Funding Information
  • National Institutes of Health (R01AG089445)

This publication has 28 references indexed in Scilit: