{
  "id": 5307,
  "url": "https://arxiv.org/abs/2604.24572v1",
  "title": "FastOMOP: A Foundational Architecture for Reliable Agentic Real-World Evidence Generation on OMOP CDM data",
  "summary": "The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), maintained by the Observational Health Data Sciences and Informatics (OHDSI) collaboration, enabled the harmonisation of electronic health records data of nearly one billion patients in 83 countries. Yet generating real-world evidence (RWE) from these repositories remains a manual process requiring clinical, epidemiological and technical expertise. LLMs and multi-agent systems have shown promise for clinical tasks, but ",
  "authors": "Niko Moeller-Grell, Shihao Shenzhang, Zhangshu Joshua Jiang, Richard JB Dobson, Vishnu V Chandrabalan",
  "category": "research",
  "topics": "healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-27T15:02:02.000Z",
  "fetched_at": "2026-07-14T16:31:40.220Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5307",
  "original_url": "https://arxiv.org/abs/2604.24572v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}