{
  "id": 3207,
  "url": "https://arxiv.org/abs/2606.28346v1",
  "title": "PySynthea: A Python-Native Framework for Scalable Synthetic Healthcare Data Generation",
  "summary": "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. Thi",
  "authors": "Roberto Cruz, David Rey-Blanco",
  "category": "research",
  "topics": "regulation,privacy-surveillance,healthcare,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-02T15:20:55.000Z",
  "fetched_at": "2026-07-14T16:30:05.530Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3207",
  "original_url": "https://arxiv.org/abs/2606.28346v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}