Semantic Convergence with LLMs for Head and Neck Cancer Quality Indicators.

Journal: Studies in health technology and informatics
Published Date: Aug 7, 2025

Abstract

We developed a novel method for leveraging large language models (LLM) to systematically filter and categorize large numbers of clinical quality indicators (CQI) for head and neck cancer. This was used to transform a tedious, human-resource intensive review process into a more efficient, knowledge-driven approach. Although we have successfully demonstrated the successful application of this approach to reduce manual effort overall, it is not possible to rely entirely on language models for such a task, and human oversight remains essential. We have delivered a generalizable approach that offers a promising pathway for more efficient and systematic clinical quality indicator management in other settings.

Authors

  • Georgina Kennedy
    Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia.
  • Marnie Harris
    University of NSW, Sydney, NSW, Australia.
  • Arya Shinde
    University of NSW, Sydney, NSW, Australia.
  • April Matt
    University of NSW, Sydney, NSW, Australia.
  • Nico Loesch
    University of NSW, Sydney, NSW, Australia.
  • Timothy Churches
    School of Clinical Medicine, University of New South Wales, Sydney, Australia.
  • Victoria Blake
    Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia; Eastern Heart Clinic, Prince of Wales Hospital, Sydney, NSW, Australia.
  • Meredith Johnston
    Liverpool Cancer Therapy Centre, South Western Sydney Local Health District.
  • Geoffrey Delaney
    Maridulu Budyari Gumal (SPHERE) Cancer Clinical Academic Group.
  • Merran Findlay
    Maridulu Budyari Gumal (SPHERE) Cancer Clinical Academic Group.

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