School of Biomedical Informatics, The University of Texas Health Science Center, Houston, Texas, United States.
Division of General Internal Medicine, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States.
Appl Clin Inform. 2022 Aug;13(4):865-873. doi: 10.1055/a-1910-4154. Epub 2022 Jul 27.
Our objective was to evaluate tokens commonly used by clinical research consortia to aggregate clinical data across institutions.
This study compares tokens alone and token-based matching algorithms against manual annotation for 20,002 record pairs extracted from the University of Texas Houston's clinical data warehouse (CDW) in terms of entity resolution.
The highest precision achieved was 99.9% with a token derived from the first name, last name, gender, and date-of-birth. The highest recall achieved was 95.5% with an algorithm involving tokens that reflected combinations of first name, last name, gender, date-of-birth, and social security number.
To protect the privacy of patient data, information must be removed from a health care dataset to obscure the identity of individuals from which that data were derived. However, once identifying information is removed, records can no longer be linked to the same entity to enable analyses. Tokens are a mechanism to convert patient identifying information into Health Insurance Portability and Accountability Act-compliant deidentified elements that can be used to link clinical records, while preserving patient privacy.
Depending on the availability and accuracy of the underlying data, tokens are able to resolve and link entities at a high level of precision and recall for real-world data derived from a CDW.
我们的目的是评估临床研究联盟常用的标记来跨机构聚合临床数据。
本研究比较了标记和基于标记的匹配算法与手动注释，以解决 20002 对记录对的实体解析问题，这些记录对是从德克萨斯大学休斯顿分校的临床数据仓库（CDW）中提取的。
使用来自姓名、性别和出生日期的标记，可实现最高精度 99.9%。使用涉及姓名、性别、出生日期和社会安全号码组合的算法，可实现最高召回率 95.5%。
为了保护患者数据的隐私，必须从医疗保健数据集删除信息，以掩盖数据来源的个人身份。但是，一旦删除了识别信息，就无法再将记录链接到同一个实体以进行分析。标记是一种将患者识别信息转换为符合《健康保险携带和责任法案》的匿名化元素的机制，这些元素可用于链接临床记录，同时保护患者隐私。
根据底层数据的可用性和准确性，标记可以在高精准度和高召回率的情况下解析和链接来自 CDW 的真实世界数据的实体。
