AI summaryⓘ
The authors studied how medication information differs between doctors' notes and structured electronic health records (EHRs) during the same patient visit. They created a method using large language models and human review to better match and normalize medication data from notes with the structured records. Their results showed that many differences come from errors in noting medications, different terms used, or timing differences in documentation rather than actual discrepancies in medication use. They also found that after improving data processing, the overlap between notes and structured medication records increased significantly. This work helps explain why medication lists might not always match perfectly across EHR sources.
electronic health recordmedication historylarge language modelmedication normalizationsemantic comparisontemporal comparisonObservational Medical Outcomes Partnershipalias mappingclinical notesstructured data
Authors
Mingyang Jiang, Congning Ni, Weixin Liu, Zhijun Yin
Abstract
Clinic notes and structured electronic health record (EHR) medication history often contain different medication information. Same-visit disagreement between these sources may result from note-side normalization errors, differences in terminology or timing, or actual differences in documentation. We developed a note-grounded approach that uses large language model (LLM) assisted reference construction, targeted and random human review, deterministic medication normalization, and semantic and temporal comparisons with structured medication history. We evaluated all normalization results on a patient-level held-out test set to limit adaptation to the study cohort. On 5,403 held-out mention rows, exact canonical agreement improved from 0.7226 with surface-exact matching to 0.8429 after lexical cleanup and curated alias mapping. In a random audit of previously unaudited rows, canonical-label agreement was 0.9210 among evaluable valid medication mentions, whereas treatment-action attribution was lower at 0.5326. In the full-cohort characterization analysis, only 16.44% of note-derived rows had same-visit exact overlap with structured medication history, but 55.17% had same-visit semantic overlap, 90.34% had same-visit or +/-30-day overlap, and only 3.97% remained in the strict no-structured-overlap bucket under broad project-level mapping. An ontology-backed sensitivity analysis further showed that held-out strict Observational Medical Outcomes Partnership (OMOP)-backed no-overlap fell from 43.99% to 36.68% after a development-derived alias supplement. These results show that note-to-structured-medication mismatch can arise from normalization errors, differences in terminology, and differences in documentation timing.