Strengthening the Brazilian Unified Health System (SUS) with Large Language Models (LLMs): Interoperability and Equity in Clinical Notes for Brazilian Public Health

Andrew Maranhão Ventura Dadario of Hospital Israelita Albert Einstein in Brazil will test and evaluate LLMs for their ability to structure and anonymize clinical notes. Such structured notes would promote data interoperability, which would improve patient care, research, and health policies for the diversity of Brazilian public health patients; and establishing standardized evaluation of LLMs for public health would promote their future applications. They will prepare a representative dataset by extracting, processing, and annotating clinical notes, and then test and compare a set of existing LLMs in extracting information from text, including their ability to preserve patient privacy, their applicability to the Portuguese language, and their suitability to primary healthcare in Brazil encompassing equitable performance across patients stratified by gender, race, ethnicity, and age. The best performing LLM will be used to transform free text from clinical notes, and the structured product will be assessed by health managers for accuracy and usefulness.