Davidson Lena, Boland Mary Regina
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania.
Institute for Biomedical Informatics, University of Pennsylvania.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:126-135. eCollection 2020.
Mapping local terminologies to standardized terminologies facilitates secondary use of electronic health records (EHR). Penn Medicine comprises multiple hospitals and facilities within the Philadelphia Metropolitan area providing services from primary to quaternary care. Our Penn Medicine (PennMed) data include medications collected during both inpatient and outpatient encounters at multiple facilities. Our goal was to map 941,198 unique medication terms to RxNorm, a standardized drug nomenclature from the National Library of Medicine (NLM). We chose three popular tools for mapping: NLM's RxMix and RxNav-in-a-Box, OHDSI's Usagi and Mayo Clinic's MedXN. We manually reviewed 400 mappings obtained from each tool and evaluated their performance for drug name, strength, form, and route. RxMix performed the best with an F1 score of 90% for drug name versus Usagi's 82% and MedXN's 74%. We discuss the strengths and limitations of each method and tips for other institutions seeking to map a local terminology to RxNorm.
将本地术语映射到标准化术语有助于电子健康记录(EHR)的二次使用。宾夕法尼亚大学医学中心在费城都会区拥有多家医院和医疗机构,提供从初级到四级护理的服务。我们宾夕法尼亚大学医学中心(PennMed)的数据包括在多个机构的住院和门诊就诊期间收集的药物信息。我们的目标是将941,198个独特的药物术语映射到RxNorm,这是美国国立医学图书馆(NLM)的标准化药物命名法。我们选择了三种常用的映射工具:NLM的RxMix和RxNav-in-a-Box、OHDSI的Usagi以及梅奥诊所的MedXN。我们人工审查了从每个工具获得的400个映射,并评估了它们在药物名称、强度、剂型和给药途径方面的性能。RxMix表现最佳,药物名称的F1分数为90%,而Usagi为82%,MedXN为74%。我们讨论了每种方法的优缺点以及其他寻求将本地术语映射到RxNorm的机构的技巧。