Leveraging Generative AI to Enhance Synthea Module Development

TL;DR

This paper investigates how large language models can assist in developing disease modules for Synthea, aiming to reduce development time, enhance diversity, and improve data quality through iterative refinement and evaluation.

Contribution

It introduces a novel approach using LLMs for Synthea module creation, including generation, evaluation, and refinement, with a focus on progressive refinement and addressing associated challenges.

Findings

LLMs can generate disease profiles and modules effectively.

Iterative evaluation improves module accuracy and quality.

Human oversight remains essential for validation.

Abstract

This paper explores the use of large language models (LLMs) to assist in the development of new disease modules for Synthea, an open-source synthetic health data generator. Incorporating LLMs into the module development process has the potential to reduce development time, reduce required expertise, expand model diversity, and improve the overall quality of synthetic patient data. We demonstrate four ways that LLMs can support Synthea module creation: generating a disease profile, generating a disease module from a disease profile, evaluating an existing Synthea module, and refining an existing module. We introduce the concept of progressive refinement, which involves iteratively evaluating the LLM-generated module by checking its syntactic correctness and clinical accuracy, and then using that information to modify the module. While the use of LLMs in this context shows promise, we also acknowledge the challenges and limitations, such as the need for human oversight, the importance of rigorous testing and validation, and the potential for inaccuracies in LLM-generated content. The paper concludes with recommendations for future research and development to fully realize the potential of LLM-aided synthetic data creation.