ArticleFrontiers in pharmacology2026
Characterization and validation of electronic medical record data for pharmacoepidemiologic research.
Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
Purpose: Electronic medical records (EMRs) are essential for pharmacoepidemiologic research; however, information on the quality of Canadian hospital EMR data is scarce. We aimed to test data validity in the Epic EMR for key pharmacoepidemiologic themes using QT-prolonging medications and major adverse cardiac events (MACEs) as the research question. Methods: An entity relationship diagram (ERD) was developed to navigate >20,000 tables. Computational data validation (comparison with Epic SlicerDicer and Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD) data) and manual data validation (chart review) were applied with iterative adjustments. Percent agreement with 95% confidence intervals was used to estimate data validity. Results: The ERD demonstrated that 43 tables are needed for key pharmacoepidemiologic research data. A cohort of 70,078 adult inpatients was used for computational validation and 2,281 patient charts for manual validation. Demographics (for example, age, sex, gender, admitting service, and critical care transfers); exposures (medication administrations); many potential confounders (lab results, interacting drugs, and electrocardiograms [EKGs]); and most timestamping (admission, medication administrations, death, lab results, and EKGs) were validated with >95% agreement. Inadequate agreement for MACE (other than death) brought the primary outcome MACE agreement to <90%, requiring patient-level linkage to CIHI-DAD to improve agreement. Comorbidities, including hypertension and heart failure, also required this linkage. Conclusion: A world-leading proprietary EMR after the painstaking development of an ERD and validation of key data fields showed that not all data are of sufficient quality for pharmacoepidemiologic research. Linked human-coded data are required for some diagnoses.
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