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

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.

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.

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.

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.

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.