De-identified Bayesian personal identity matching for privacy-preserving record linkage despite errors: development and validation.

ABSTRACT:

Background

Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, and (2) a requirement to link databases without a common person-unique identifier.

Methods

We develop a Bayesian matching technique to solve both. We provide an open-source software implementation capable of de-identified probabilistic matching despite discrepancies, via fuzzy representations and complete mismatches, plus de-identified deterministic matching if required. We validate the technique by testing linkage between multiple medical records systems in a UK National Health Service Trust, examining the effects of decision thresholds on linkage accuracy.

SUBMITTER: Cardinal RN 

PROVIDER: S-EPMC10163749 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature