{
  "abstract": "BackgroundAmbient artificial-intelligence (AI) scribes are being deployed beyond medicine into allied-health practice, but comparative evidence mainly concerns physician documentation.\n\nObjectiveTo compare quality and transcript-grounded clinical-risk error burden of AI-generated and allied health professional notes from identical simulated consultations.\n\nMethodsPaired simulation across dietetics, physiotherapy, occupational therapy and speech and language therapy (five consultations each; 20 pairs; 40 notes). Heidi generated an unedited first-draft note and a professional documented each encounter. Professionals rated PDQI-9 quality. Professional and automated review identified unsupported statements and omissions; two language models graded clinical risk. Co-primary outcomes were PDQI-9 score and Critical+High error density.\n\nResultsMean PDQI-9 scores were 38.45 and 35.05 for AI and professional notes (paired difference +3.40, 95% CI +0.01 to +6.79; Wilcoxon p=0.042; dz=0.47). The Bayesian estimate was +3.47 (94% equal-tailed credible interval +0.66 to +6.30; P[AI>professional]=0.989). Automated-reviewer all-error density was 3.35 vs 8.55 per note (professional-minus-AI difference 5.20, 95% CI 3.85 to 6.55); omission density was 1.45 vs 5.20 (difference 3.75, 2.80 to 4.65). Critical+High density was 0.35 vs 0.75 (difference 0.40, -0.10 to 1.00). The pooled quality result was driven principally by dietetics.\n\nConclusionsAI-generated notes achieved modestly higher pooled quality and fewer transcript-grounded errors, chiefly omissions; serious-error evidence was inconclusive. Small discipline samples and incomplete ground truth preclude safety or discipline-wide superiority claims.",
  "abstract_shown": true,
  "affiliations": [
    "Heidi Health"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "H. I. Bergman"
    },
    {
      "name": "V. Liu"
    },
    {
      "name": "S. Ali"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/df50zegr5dgwh.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.16.26365719",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365719v1.full",
  "id": "df50zegr5dgwh",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365719v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7222934117",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365719v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/df50zegr5dgwh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:10Z",
      "native_id": "10.64898/2026.10.16.26365719",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.16.26365719v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Quality and transcript-grounded clinical-risk error burden of AI-generated vs allied health professional clinical notes: a paired simulation study across four disciplines",
  "title_text": "Quality and transcript-grounded clinical-risk error burden of AI-generated vs allied health professional clinical notes: a paired simulation study across four disciplines",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:10Z",
  "url": "https://research.draconitic.com/paper/df50zegr5dgwh",
  "venue": "medRxiv",
  "versions": []
}