Mendoza Daniel, Amanda Isca, Zhao Lin, Chern Darwyn, Grando Maria Adela
College of Health Solutions, Arizona State University, Phoenix, AZ, USA.
HonorHealth, Scottsdale, AZ, USA.
Health Informatics J. 2025 Jan-Mar;31(1):14604582251316781. doi: 10.1177/14604582251316781.
Show the generalizability of an ingredient-based method to automatically create an up-to-date, error-free, complete list of medication codes (e.g., opioid medications with at least one opioid ingredient) from an ingredient list (e.g., opioid ingredients). The method, previously evaluated with the RxNorm terminology, was reused and applied in the National Drug Code (NDC) context to create opioid and antidepressant medication lists. The resulting medication lists were validated through automatic comparisons with curated medication lists (the CDC opioid medication code set and the HEDIS antidepressant medication code set), automatic comparisons with active medication lists (Federal Drug Administration (FDA) databases and RxNorm), and manual physicians' review. The proposed ingredient-based method was validated with two clinical terminologies (RxNorm and NDC) and two use cases (opioid and antidepressant medication code sets), demonstrating generalizability, reusability, and high accuracy. Methodologies for creating lists of sensitive codes are essential to supporting patients' need to restrict access to potentially stigmatizing information. In contrast with data-driven, less accurate, and unexplainable methods to create clinical lists, our study innovated by proposing algorithms to automatically discover correct, complete, up-to-date, and ingredient-based medication lists.
展示一种基于成分的方法的通用性，该方法可从成分列表（如阿片类成分）自动创建一份最新、无错误、完整的药物代码列表（如含有至少一种阿片类成分的阿片类药物）。之前在RxNorm术语体系下评估过的该方法，被重新使用并应用于国家药品代码（NDC）环境中，以创建阿片类药物和抗抑郁药物列表。通过与精心整理的药物列表（疾病控制与预防中心阿片类药物代码集和医疗效果数据信息集抗抑郁药物代码集）进行自动比较、与活性药物列表（美国食品药品监督管理局（FDA）数据库和RxNorm）进行自动比较以及医生人工审核，对生成的药物列表进行验证。所提出的基于成分的方法在两种临床术语体系（RxNorm和NDC）以及两个用例（阿片类药物和抗抑郁药物代码集）中得到验证，证明了其通用性、可重用性和高精度。创建敏感代码列表的方法对于满足患者限制获取潜在污名化信息的需求至关重要。与数据驱动的、准确性较低且无法解释的创建临床列表的方法不同，我们的研究通过提出算法来自动发现正确、完整、最新且基于成分的药物列表进行了创新。
