A naturalistic patient matching algorithm: Derivation and validation

With the vast amount of medical data being generated on an electronic basis, to ensure that individual patient information is properly linked across multiple sources, efficient patient matching algorithms are critical. We describe a novel naturalistic approach that is a hybrid of both deterministic (exact) and probabilistic ("close") systems that allows for manual adjudication of cases where necessary, but whose basis stems from the thought processes that a rational individual would follow in the comparison of medical records. A validation of this algorithm using large databases from two disparate sources demonstrates that the naturalistic approach can largely eliminate false positives (false matches) and false negatives (false mismatches).