Abstract
Objective To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research. Methods The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches. Results The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%. Conclusions Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.
Original language | English (US) |
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Pages (from-to) | 1072-1080 |
Number of pages | 9 |
Journal | Journal of the American Medical Informatics Association |
Volume | 22 |
Issue number | 5 |
DOIs | |
State | Published - Sep 2015 |
Funding
The authors would like to acknowledge the exceptional work of Ariadna Garcia, Pravin Babu, Shazia Sathar, and Ulas Keles in drafting an initial version of the hashing and matching software. Kominers gratefully acknowledges the support of the Harvard Milton Fund.
Keywords
- Health information exchange
- Privacy protection
- Record linkage
ASJC Scopus subject areas
- Health Informatics