Frontiers in pharmacology · 2026
Characterization and validation of electronic medical record data for pharmacoepidemiologic research.
СтатьяНазвание и аннотация: Перевод в очереди
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Schneider Tyler, Punjani Tanvi, Silva Jessyca Matos, Mbuagbaw Lawrence, Gibson Paul, Holbrook Anne M
Аннотация
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.
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.