PySynthea: A Python-Native Framework for Scalable Synthetic Healthcare Data Generation

Abstract

Synthetic healthcare data is increasingly important for research, education, and machine learning development where access to real patient data is limited by privacy and governance constraints. While Synthea provides a widely adopted framework for generating realistic longitudinal electronic health record data, its current implementation presents adoption barriers for many researchers and data scientists due to deployment complexity and limited integration with modern Python-based workflows. This paper introduces PySynthea, a Python-native reimplementation of Synthea designed to improve accessibility, extensibility, and interoperability within the scientific Python ecosystem. The framework provides modular synthetic patient generation, configurable healthcare simulation pipelines, and support for standard healthcare data formats while integrating naturally with tools such as pandas and machine learning workflows. By reducing operational complexity and aligning synthetic data generation with the dominant data science ecosystem, PySynthea aims to accelerate experimentation and broaden the use of synthetic healthcare data in research and applied AI development. The code in this github repository https://github.com/TIET-AI/tietai-synthea.