Abstract
Abstract
Objective
We aimed to develop and validate an automated, scalable framework to harmonize fragmented UK primary care prescription records into a research-ready dataset by mapping diverse medical ontologies to a unified reference standard from the UK Biobank.
Materials and methods
Preprocessing involved selecting a single drug code where multiple were recorded, cleaning codes to match reference presentations, expanding code granularity via drug descriptions, and updating outdated codes. Harmonization mapped British National Formulary (BNF) and Read2 codes to dm+d, the National Health Service standard vocabulary, with records then homogenized to the Virtual Medicinal Product (VMP) granularity. We validated our approach by profiling prescribing patterns across 312 diseases.
Results
We preprocessed 57 659 844 records from 221 868 participants; we dropped 48 950 records for missing drug codes and 13% used multiple ontologies. Most records were BNF-encoded (76%), and 49% required granularity expansion via drug description. In total, 72% of records were harmonized to dm+d, of which 99.98% were converted to VMP. Across 312 diseases, we identified 23 352 disease-drug associations involving 237 medications (BNF subparagraphs) that survived statistical correction, most resembling drug-indication pairs.
Discussion
Our framework addresses a longstanding barrier to large-scale pharmacoepidemiological research by reconciling heterogeneous coding systems into a single, consistent standard. The high conversion rate to VMP demonstrates near-complete coverage, while the recovery of plausible drug-indication associations supports validity. Limitations include residual uncodable records and dependence on dm+d completeness.
Conclusion
Our approach transforms fragmented prescription records into a streamlined, enriched dataset suitable for large-scale discovery research.