{
  "id": 17501,
  "url": "https://www.jmir.org/2026/1/e84454",
  "title": "Machine Learning to Identify Point-of-Care Ultrasound and Evaluate Standardized Documentation: Retrospective Operational Cohort Study",
  "summary": "Background: Point-of-care ultrasound (POCUS) is integral to obstetrics and gynecology (OBGYN), offering bedside diagnostic and therapeutic advantages. Despite its widespread adoption, accurate documentation and billing remain challenging due to inconsistent workflows, variable free-text note quality, and inefficiencies within electronic health record (EHR) systems. These barriers often result in missed procedural charges and hinder operational, educational, and reimbursement efforts. Objective:",
  "authors": "Kevin Nguyen, Zewen Wu, Chu-An Tsai, John Vandervest, D’Anna Lammers, Ruth Cassidy, Zachary Murphy, Balaji Pandian, Maya M Hammoud, Jennifer Collin, Roger Smith, Rosalyn Maben-Feaster, Amy Kaufman Eddy, Michael L Burns",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T21:45:03.000Z",
  "fetched_at": "2026-08-08T05:10:34.355Z",
  "source_slug": "x-jmir-journal-of-medical-internet-researc",
  "source_name": "JMIR (Journal of Medical Internet Research)",
  "source_homepage": "https://www.jmir.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17501",
  "original_url": "https://www.jmir.org/2026/1/e84454",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}