Language Models for Automated Patient Record Linkage
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This research explores the use of language models to automate patient record linkage, a crucial process for integrating fragmented healthcare data. The study investigates the effectiveness of these models for two key tasks: blocking, which reduces the number of record pairs to compare, and matching, which determines if two records belong to the same patient. Using real-world cancer registry data, the authors fine-tuned and evaluated various language models, comparing their performance against traditional methods. The findings indicate that fine-tuned large language models excel at matching, achieving high accuracy with minimal errors, although a hybrid approach might be more effective for blocking. The study highlights the potential of these advancements to improve efficiency and data integration in healthcare.
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