Best Practices in Evolving Privacy Frameworks for Patient Age Data: Census Data Study.

BACKGROUND: Over the previous 4 decennial censuses, the population of the United States has grown older, with the proportion of individuals aged at least 90 years old in the 2010 census being more than 2 and a half times what it was in the 1980 census. This suggests that the threshold for constraining age introduced in the Safe Harbor method of the HIPAA (Health Insurance Portability and Accountability Act) in 1996 may be increased without exceeding the original levels of risk. This is desirable to maintain or even increase the utility of affected data sets without compromising privacy. OBJECTIVE: In light of the upcoming release of 2020 census data, this study presents a straightforward recipe for updating age-constrained thresholds in the context of new census data and derives recommendations for new thresholds from the 2010 census. METHODS: Using census data dating back to 1980, we used group size considerations to analyze the risk associated with various maximum age thresholds over time. We inferred the level of risk of the age cutoff of 90 years at the time of HIPAA's inception in 1996 and used this as a baseline from which to recommend updated cutoffs. RESULTS: The maximum age threshold may be increased by at least 2 years without exceeding the levels of risk conferred in HIPAA's original recommendations. Moreover, in the presence of additional information that restricts the population in question to a known subgroup with increased longevity (for example, restricting to female patients), the threshold may be increased further. CONCLUSIONS: Increasing the maximum age threshold would enable the data user to gain more utility from the data without introducing risk beyond what was originally envisioned with the enactment of HIPAA. Going forward, a recurring update of such thresholds is advised, in line with the considerations detailed in the paper.

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.

The Impact of Name Transformation on Match Rates Within a Large Consumer Database.

Accurate record linkage depends on the availability and quality of features such as first name and last name. Privacy preserving record linkage methods using tokenization is sensitive to perturbations in the patient features used as inputs. In this study we evaluated the impact of name transformations on the accuracy of patient matching using a large commercial dataset. We used a set of 68 million records representing 59 million unique individuals, and implemented and evaluated eight name transformation strategies, and generated precision, recall and F1 scores. Transforming names to include the most common nicknames resulted in a significant gain in recall while maintaining precision, and generated the highest F1 score compared with no name transformation (0.905 vs 0.807). Strategies tailored to transforming patient features can improve the precision and recall of patient matching, and make it possible to create high quality, linked datasets for research purposes.