Nourelahi Mehdi, Sadhu Eugene M, Samayamuthu Malarkodi J, Visweswaran Shyam
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, United States.
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, United States.
J Am Med Inform Assoc. 2025 Jun 1;32(6):1066-1070. doi: 10.1093/jamia/ocaf061.
The primary objective was to compile a comprehensive list of Logical Observation Identifiers Names and Codes (LOINC) terms that may be associated with patient, healthcare provider, and healthcare facility identifying information.
We developed a 2-step procedure for identifying LOINC terms, which consists of a keyword search of Long Common Names and filtering on selected property values, followed by expert physician review to confirm and categorize the terms.
The final list comprises 1309 LOINC terms potentially associated with identifying information of patients, providers, and facilities. This list is publicly available on GitHub.
Compared with electronic health record data coded with other terminologies, LOINC-coded data present unique challenges for deidentification, and a resource of LOINC terms that may be associated with identifying information will be helpful for this purpose.
This resource is valuable for deidentifying LOINC-coded data, ensuring compliance with the Health Insurance Portability and Accountability Act (HIPAA), and preserving the privacy of patients, providers, and facilities.
主要目标是编制一份可能与患者、医疗服务提供者和医疗机构识别信息相关的逻辑观察标识符名称和代码（LOINC）术语的综合列表。
我们开发了一个两步程序来识别LOINC术语，该程序包括对长通用名称进行关键词搜索并根据选定的属性值进行筛选，随后由专家医生进行审查以确认术语并进行分类。
最终列表包含1309个可能与患者、提供者和机构识别信息相关的LOINC术语。此列表可在GitHub上公开获取。
与使用其他术语编码的电子健康记录数据相比，使用LOINC编码的数据在去识别方面存在独特挑战，而一份可能与识别信息相关的LOINC术语资源将有助于实现这一目的。
该资源对于对LOINC编码数据进行去识别、确保符合《健康保险流通与责任法案》（HIPAA）以及保护患者、提供者和机构的隐私具有重要价值。
