Real-World Matching Performance of Deidentified Record-Linking Tokens.

Objective: Our objective was to evaluate tokens commonly used by clinical research consortia to aggregate clinical data across institutions.

Methods: 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.

Results: 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.

Discussion: 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.

Conclusion: 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.