Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
This addresses healthcare data interoperability for medical professionals, but it is incremental as it applies an existing LLM method to a specific domain.
The study tackled the problem of converting clinical texts into FHIR resources using a large language model, achieving over 90% accuracy on 3,671 text snippets and streamlining multi-step processes.
In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. We leveraged the LLM to convert clinical texts into their corresponding FHIR resources. Our experiments, conducted on 3,671 snippets of clinical text, demonstrated that the LLM not only streamlines the multi-step natural language processing and human calibration processes but also achieves an exceptional accuracy rate of over 90% in exact matches when compared to human annotations.