{
  "abstract": "ObjectiveVenous thromboembolism (VTE) is a leading cause of preventable inpatient mortality, while the real-world performance of mandated risk assessment and the potential for automating using electronic health record (EHR) data remain unclear. We evaluated completion, concordance with structured EHR data, clinical validity, and feasibility of EHR-supported VTE risk assessment assisted by machine learning.\n\nMaterials and MethodsWe analysed 577,904 admissions and 726,896 VTE assessment forms across five NHS hospitals between 2015 and 2025. Concordance and clinical validity were evaluated in 87,118 completed assessments from 2023 to 2024. Machine learning models compared clinician-recorded and EHR-derived risk factors in 74,372 encounters with an assessment completed within 14 hours of admission.\n\nResultsOverall completion was 96.7%, and timely completion improved from 47.4% in 2015 to 90.5% in 2024. Agreement between forms and EHR data was good for common risk factors (e.g. age [≥] 60 years: precision 0.95, recall 0.77), but low-prevalence variables were often under-documented in the forms (e.g. critical care admission: precision 0.20, recall 0.14). Despite these discrepancies, form-derived thrombosis risk was associated with increased VTE incidence (adjusted OR 3.31, 95% CI 2.81-3.90). Models using EHR-derived factors available within 14 hours achieved an AUROC of 0.709, compared with 0.704 for clinician-recorded variables.\n\nDiscussionMuch of the information the risk assessment requires already exists in structured EHR data, and EHR-derived variables contained discriminative information comparable to that in clinician-recorded factors.\n\nConclusionRoutinely collected EHR data could support real-time pre-population of structured VTE assessments, thereby reducing documentation burden while preserving clinical validity.",
  "abstract_shown": true,
  "affiliations": [
    "Imperial College London - St Mary's Campus"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Z. Li"
    },
    {
      "name": "E. Yagis"
    },
    {
      "name": "A. Riad"
    },
    {
      "name": "O. Windrath-Carr"
    },
    {
      "name": "M. Arribas"
    },
    {
      "name": "T. Sodiq"
    },
    {
      "name": "K. Goldsmith"
    },
    {
      "name": "B. Glampson"
    },
    {
      "name": "K. Flott"
    },
    {
      "name": "G. Haji"
    },
    {
      "name": "Z. Khan"
    },
    {
      "name": "C. Baker"
    },
    {
      "name": "E. K. Mayer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7xhs5pcrk303n.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.18.26360687",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.18.26360687v2.full",
  "id": "7xhs5pcrk303n",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.18.26360687v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2724|Venous Thromboembolism Diagnosis and Management"
    ]
  ],
  "openalex": "W7203773519",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.18.26360687v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7xhs5pcrk303n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:32Z",
      "native_id": "10.64898/2026.08.18.26360687",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.18.26360687v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Machine Learning-Supported Efficient VTE Risk Assessment using Routinely Collected Electronic Health Record Data",
  "title_text": "Machine Learning-Supported Efficient VTE Risk Assessment using Routinely Collected Electronic Health Record Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:32Z",
  "url": "https://research.draconitic.com/paper/7xhs5pcrk303n",
  "venue": "medRxiv",
  "versions": []
}