aceAn informatics framework to harmonize electronic health record medication data for managed care analytics and artificial intelligence applications
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…
A two-layered RxCUI ingredient and ATC framework harmonized 214,080 discharge medication records from older adults into standardized representations, achieving 100% initial mapping via deterministic crosswalks to support transportable managed care AI tools.
Heterogeneous NDC, Multum, and RxCUI identifiers in real-world EHRs undermined semantic consistency, with over half of records needing string reconciliation and up to 57.4% requiring correction due to branded formulation omissions and indication- or route-based ATC ambiguities.
Framework evaluated only in adults 65+ at a single tertiary center and required substantial manual correction due to crosswalk omissions, lexical misclassifications, and taxonomic ambiguities requiring clinical context.
Evidence
- Peer-reviewedJournal of Managed Care & Specialty Pharmacy2026-09-01
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Truvace Impact Record TRV-2026-0957, v1: “An informatics framework to harmonize electronic health record medication data for managed care analytics and artificial intelligence applications.” Truvace, 2026-09-01. /record/TRV-2026-0957 (accessed at citation time). sha256 f6a38bcf0b7a66ed…
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