{
  "id": 5190,
  "url": "https://arxiv.org/abs/2604.27309v1",
  "title": "End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians",
  "summary": "Clinical AI systems require not just point-in-time evaluation but continuous governance: the ongoing practice of monitoring, evaluating, iterating, and re-evaluating performance throughout deployment. We present an end-to-end framework of governance that integrates rubric validation, live deployment feedback, technical performance monitoring, and cost tracking, with controlled experimentation gating system changes before deployment. Applied to Hyperscribe, an EHR-embedded agent that converts amb",
  "authors": "Aaryan Shah, Andrew Hines, Alexia Downs, Denis Bajet, Paulius Mui, Fabiano Araujo et al.",
  "category": "research",
  "topics": "regulation,privacy-surveillance,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-30T01:45:39.000Z",
  "fetched_at": "2026-07-14T16:31:35.573Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5190",
  "original_url": "https://arxiv.org/abs/2604.27309v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}