An informatics framework to harmonize electronic health record medication data for managed care analytics and artificial intelligence applications
Abstract
BACKGROUND: Artificial intelligence (AI) applications in managed care pharmacy depend on semantically consistent medication data, yet heterogeneous medication identifiers across real-world electronic health records (EHRs) could undermine analytic fidelity and risk propagating classification errors. To enable transportable, reproducible AI tools, methods for harmonizing disparate medication identifiers (eg, National Drug Code [NDC] and Multum drug synonym ID) to standardized vocabularies are required.
OBJECTIVE: To develop and evaluate an informatics framework that harmonizes heterogeneous medication identifiers into standardized ingredient- and pharmacologic-class representations, establishing the semantic integrity required to support transparent, reliable downstream managed care analytics and AI developments.
METHODS: We conducted a retrospective analysis of discharge medication records, excluding health supplements and nonmedicated agents, from EHRs of adults aged 65 years or older who had at least 1 hospitalization between 2020 and 2024 at a tertiary medical center (Buffalo General Medical Center). Heterogeneous identifiers (Multum, NDC, RxCUI) were harmonized using a 2-layered architecture anchored to RxCUI ingredient [IN] concepts and abstracted to Anatomical Therapeutic Chemical (ATC) classification. Deterministic crosswalks were derived from the RxNorm Full Monthly Release and complemented with a validated TriNetX reference file. Unmapped records underwent structured…
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Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
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