{
  "abstract": "Decision models like Jev read text and return probabilities, categories, or ordinal levels, with confidence scores for the latter two, at a fraction of large language models (LLMs) cost. We explored how Jev could be integrated into clinical workflows for all these output types: mortality risk estimation, categorical Choosing-Wisely decisions for patient questions, and ordinal colon cancer staging. With standard LLM-style inputs, Jev trailed five flagship LLMs: mortality AUROC was 0.822 versus 0.837-0.846; Choosing-Wisely accuracy, 87.7% versus 98.4-100%; and staging accuracy, 45.4% versus 94.2-100%. Developer-recommended input alterations improved staging accuracy to 75.8% with medical context, 82.1% with task decomposition, and 85.8% with both. None of Jevs staging errors had full confidence. Using Jev for fully confident staging decisions and LLMs for the rest was non-inferior to each LLM alone and cut flagship LLM costs by 33-44%. Prompted as designed, decision models may efficiently share structured clinical tasks with LLMs.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Family Medicine, Clalit Health Services"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "D. Ariel"
    },
    {
      "name": "G. Laban"
    },
    {
      "name": "S. Endelman"
    },
    {
      "name": "R. Balicer"
    },
    {
      "name": "N. Dagan"
    },
    {
      "name": "S. Cohen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/83tqt3fh35fse.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.09.26365130",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26365130v1.full",
  "id": "83tqt3fh35fse",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26365130v1",
  "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": "W7214978480",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26365130v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/83tqt3fh35fse.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:42Z",
      "native_id": "10.64898/2026.10.09.26365130",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.09.26365130v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Decision models can offload structured clinical tasks from large language models",
  "title_text": "Decision models can offload structured clinical tasks from large language models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:42Z",
  "url": "https://research.draconitic.com/paper/83tqt3fh35fse",
  "venue": "medRxiv",
  "versions": []
}