AI 中文总结
本研究针对门诊病历与结构化EHR药物史的药物信息差异，开发结合LLM、人工审核等的标准化方法，通过患者级测试集验证，明确不匹配原因并提升了药物信息一致性。
AI 中文摘要
门诊病历与结构化电子健康记录（EHR）药物史常包含不同的药物信息，同一就诊期间两类来源的不一致可能源于病历侧的标准化错误、术语或时间差异，或实际记录差异。我们开发了一种以病历为基础的方法，该方法采用大语言模型（LLM）辅助参考构建、定向与随机人工审核、确定性药物标准化，以及与结构化药物史的语义和时间比较。我们在患者级留出测试集上评估所有标准化结果，以限制对研究队列的适配。在5403条留出的提及行中，精确规范一致性从表面精确匹配的0.7226提升至词汇清理和精心策划的别名映射后的0.8429。在对先前未审核行的随机审核中，可评估有效药物提及的规范标签一致性为0.9210，而治疗行为归因较低，为0.5326。在全队列刻画分析中，仅16.44%的病历衍生行与结构化药物史存在同一就诊期间的精确重叠，但55.17%存在同一就诊期间的语义重叠，90.34%存在同一就诊期间或±30天的重叠，在广泛项目级映射下，仅3.97%仍处于严格无结构化重叠类别。基于本体的敏感性分析进一步显示，留出的严格观察性医疗结果合作伙伴（OMOP）支持的无重叠比例，在使用开发衍生的别名补充后从43.99%降至36.68%。这些结果表明，病历到结构化药物的不匹配可源于标准化错误、术语差异和记录时间差异。
英文摘要
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.
Comments9 pages, 3 figures. Submitted to IEEE BIBM 2026
