Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.
Department of Learning Health Sciences, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA.
J Am Med Inform Assoc. 2022 Aug 16;29(9):1471-1479. doi: 10.1093/jamia/ocac096.
To determine the variability of ingredient, strength, and dose form information from drug product descriptions in real-world electronic prescription (e-prescription) data.
A sample of 10 399 324 e-prescriptions from 2019 to 2021 were obtained. Drug product descriptions were analyzed with a named entity extraction model and National Drug Codes (NDCs) were used to get RxNorm Concept Unique Identifiers (RxCUI) via RxNorm. The number of drug product description variants for each RxCUI was determined. Variants identified were compared to RxNorm to determine the extent of matching terminology used.
A total of 353 002 unique pairs of drug product descriptions and NDCs were analyzed. The median (1st-3rd quartile) number of variants extracted for each standardized expression in RxNorm, was 3 (2-7) for ingredients, 4 (2-8) for strength, and 41 (11-122) for dosage forms. Of the pairs, 42.35% of ingredients (n = 328 032), 51.23% of strengths (n = 321 706), and 10.60% of dose forms (n = 326 653) used matching terminology, while 16.31%, 24.85%, and 13.05% contained nonmatching terminology, respectively.
A wide variety of drug product descriptions makes it difficult to determine whether 2 drug product descriptions describe the same drug product (eg, using abbreviations to describe an active ingredient or using different units to represent a concentration). This results in patient safety risks that lead to incorrect drug products being ordered, dispensed, and used by patients. Implementation and use of standardized terminology may reduce these risks.
Drug product descriptions on real-world e-prescriptions exhibit large variation resulting in unnecessary ambiguity and potential patient safety risks.
确定真实世界电子处方(e-prescription)数据中药物产品描述的成分、强度和剂型信息的可变性。
获取了 2019 年至 2021 年的 10399324 份电子处方样本。使用命名实体提取模型分析药物产品描述,并使用国家药物代码(NDC)通过 RxNorm 获得 RxNorm 概念唯一标识符(RxCUI)。确定每个 RxCUI 的药物产品描述变体数量。比较鉴定出的变体与 RxNorm,以确定使用的匹配术语的程度。
共分析了 353002 对唯一的药物产品描述和 NDC。RxNorm 中每个标准化表达提取的变体中位数(1 四分位数-3 四分位数)为 3(2-7)个成分、4(2-8)个强度和 41(11-122)个剂型。在这些对中,42.35%的成分(n=328032)、51.23%的强度(n=321706)和 10.60%的剂型(n=326653)使用匹配术语,而分别有 16.31%、24.85%和 13.05%的成分、强度和剂型包含不匹配术语。
药物产品描述的种类繁多,难以确定 2 种药物产品描述是否描述了相同的药物产品(例如,使用缩写描述活性成分或使用不同的单位表示浓度)。这会导致患者安全风险,导致错误的药物产品被订购、配药和患者使用。实施和使用标准化术语可能会降低这些风险。
真实世界电子处方上的药物产品描述存在很大差异,导致不必要的模糊性和潜在的患者安全风险。