Leveraging Generative AI to Enhance Synthea Module Development
This work addresses the challenge of creating synthetic health data for researchers and developers, but it is incremental as it applies existing LLM methods to a new domain without major breakthroughs.
The paper tackled the problem of developing disease modules for Synthea, a synthetic health data generator, by using large language models (LLMs) to assist in the process, resulting in potential reductions in development time and expertise requirements, though no concrete numbers were provided.
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.