A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms. (28th October 2022) - Gupta, Agrayan K; Kasthurirathne, Suranga N; Xu, Huiping; Li, Xiaochun; Ruppert, Matthew M; Harle, Christopher A; Grannis, Shaun J
Abstract: Healthcare systems are hampered by incomplete and fragmented patient health records. Record linkage is widely accepted as a solution to improve the quality and completeness of patient records. However, there does not exist a systematic approach for manually reviewing patient records to create gold standard record linkage data sets. We propose a robust framework for creating and evaluating manually reviewed gold standard data sets for measuring the performance of patient matching algorithms. Our 8-point approach covers data preprocessing, blocking, record adjudication, linkage evaluation, and reviewer characteristics. This framework can help record linkage method developers provide necessary transparency when creating and validating gold standard reference matching data sets. In turn, this transparency will support both the internal and external validity of recording linkage studies and improve the robustness of new record linkage strategies.