{
  "abstract": "BackgroundPostpolypectomy surveillance intervals derived from colonoscopy findings and histopathology should be guideline-based, yet these are frequently not adhered to, contributing to under- and oversurveillance. This motivates a more structured and automated approach to improve adherence. ObjectiveWe developed and evaluated an automated, implementable, guideline-aligned decision support tool that extracts colorectal polyp features from free-text colonoscopy and histopathology reports and produces auditable surveillance recommendations. MethodsWe extracted paired colonoscopy and histopathology reports from a National Health Service (NHS) trust and designed a 4-stage prompting pipeline using open-weight large language models to separate multispecimen histopathology reports into individual containers (“pots”), align endoscopic findings with specimens, and normalize each pot into a structured schema. The surveillance intervals were then computed by a deterministic rule engine encoding guideline rules. We evaluated extraction performance on 100 procedures (215 pots), assessed surveillance recommendation performance on a same-site cohort (n=100), and performed external validation on a cohort from a second trust (n=100). A separate retrospective audit compared automated recommendations and observed routine scheduling against a clinician-derived reference based on full electronic health record review in 127 patients. ResultsKey extraction fields showed end-to-end accuracy (specimen type=0.995; location=0.981; diagnosis=0.981; excision completeness=0.949), with mean report-level pot comprehensiveness of 0.968 (SD 0.112). Overall surveillance recommendation accuracy was 0.96 (95% CI 0.90-0.98) at Guy’s and St Thomas’ NHS Foundation Trust (GSTT) and 0.93 (95% CI 0.86-0.97) at Sandwell and West Birmingham NHS Trust (SWB) and showed strong class-level performance (F1F1F1F1 ConclusionsThis hybrid approach using open-weight large language models for structured extraction and guideline-encoded rules for decision-making can generate accurate, lightweight, and auditable postpolypectomy surveillance recommendations. Such a tool has the potential to improve safety netting and guideline adherence in NHS colonoscopy workflows.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Agathe Zecevic"
    },
    {
      "name": "Sabina De Martino",
      "orcid": "0000-0002-9513-5164"
    },
    {
      "name": "Joshua Shan Chen Lee",
      "orcid": "0009-0000-9969-1929"
    },
    {
      "name": "Victoria To",
      "orcid": "0009-0006-8219-8285"
    },
    {
      "name": "Vedant Bhardwaj",
      "orcid": "0009-0007-9094-9592"
    },
    {
      "name": "Rhea Sibal",
      "orcid": "0009-0006-3393-4493"
    },
    {
      "name": "Nigel Trudgill",
      "orcid": "0000-0002-8040-8158"
    },
    {
      "name": "Shahd A Mohamed",
      "orcid": "0000-0001-5311-3090"
    },
    {
      "name": "Angus Roberts",
      "orcid": "0000-0002-4570-9801"
    },
    {
      "name": "Sebastian Zeki",
      "orcid": "0000-0003-1673-2663"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1trmp4fp8ppe5.bib",
  "cited_by": 0,
  "doi": "10.2196/102789",
  "fulltext_url": "https://www.jmir.org/2026/1/e102789",
  "id": "1trmp4fp8ppe5",
  "issns": [
    "1438-8871"
  ],
  "landing_url": "https://doi.org/10.2196/102789",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R858-859.7"
    ],
    [
      "lcc",
      "RA1-1270"
    ],
    [
      "openalex",
      "2730|Colorectal Cancer Screening and Detection"
    ]
  ],
  "openalex": "W7222610296",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "JMIR Publications",
  "ris": "https://research.draconitic.com/paper/1trmp4fp8ppe5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:14Z",
      "native_id": "36ab105585f64588ae9ce7862cd6c2d8",
      "source": "doaj",
      "url": "https://doi.org/10.2196/102789"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Colorectal Cancer Screening and Detection",
      "subfield": "oncology"
    }
  ],
  "title": "Improving Postpolypectomy Surveillance Decisions Using an Automated Analysis of Colonoscopy and Histopathology Reports: 2-Center Validation Study",
  "title_text": "Improving Postpolypectomy Surveillance Decisions Using an Automated Analysis of Colonoscopy and Histopathology Reports: 2-Center Validation Study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:14Z",
  "url": "https://research.draconitic.com/paper/1trmp4fp8ppe5",
  "venue": "Journal of Medical Internet Research",
  "versions": []
}