{
  "id": 6998,
  "url": "https://arxiv.org/abs/2604.09630v1",
  "title": "Adoption and Effectiveness of AI-Based Anomaly Detection for Cross Provider Health Data Exchange",
  "summary": "This study investigates the adoption and effectiveness of AI-based anomaly detection in cross-provider electronic health record (EHR) environments. It aims to (1) identify the organisational and digital capabilities required for successful implementation and (2) evaluate the performance and interpretability of lightweight anomaly detection approaches using contextual audit data. A semi-systematic scoping synthesis is conducted to derive a four-pillar readiness framework covering governance, infr",
  "authors": "Cao Tram Anh Hoang",
  "category": "research",
  "topics": "regulation,safety-alignment,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-19T15:22:02.000Z",
  "fetched_at": "2026-07-14T16:32:54.533Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6998",
  "original_url": "https://arxiv.org/abs/2604.09630v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}