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Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas

Stephen Ali, Thomas Dobbs, Matt Jovic, Huw Strafford, Beata Fonferko-Shadrach, Arron Lacey Orcid Logo, Namor Williams, Owen Pickrell Orcid Logo, Hayley Hutchings Orcid Logo, Iain Whitaker

British Journal of Surgery, Volume: 110, Issue: 9, Pages: 1072 - 1075

Swansea University Authors: Stephen Ali, Thomas Dobbs, Matt Jovic, Huw Strafford, Beata Fonferko-Shadrach, Arron Lacey Orcid Logo, Owen Pickrell Orcid Logo, Hayley Hutchings Orcid Logo, Iain Whitaker

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DOI (Published version): 10.1093/bjs/znad055

Abstract

Accurate and accessible outcomes following a cancer diagnosis are crucial in maintaining robust quality assurance. Multidisciplinary team (MDT) meetings aim to improve care through group consensus, national guidance, clear documentation, and communication. However, research has highlighted limitatio...

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Published in: British Journal of Surgery
ISSN: 0007-1323 1365-2168
Published: Oxford University Press (OUP) 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa63002
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Abstract: Accurate and accessible outcomes following a cancer diagnosis are crucial in maintaining robust quality assurance. Multidisciplinary team (MDT) meetings aim to improve care through group consensus, national guidance, clear documentation, and communication. However, research has highlighted limitations in their outputs, especially regarding the way outcomes are databased and audited1. Novel technologies, such as artificial intelligence (AI), have the potential to improve this, as cited in the Royal College of Surgeons of England ‘Future of Surgery’ commission2.Natural language processing (NLP), a form of AI, offers a novel approach to automate extraction of detailed clinical information from unstructured electronic healthcare record data, such as clinic letters, operative notes, and histopathology reports. In a recent systematic review, NLP was found to have higher sensitivity and comparable specificity in identifying postoperative complications compared to conventional administrative methods3.To date, no studies have used NLP to determine incomplete excision rates in surgical oncology. In this study, the feasibility of automatically extracting and interpreting margin status from histopathology reports using an NLP-based system was demonstrated.
Keywords: General surgery, plastic surgery
College: Faculty of Medicine, Health and Life Sciences
Funders: S.R.A. and T.D.D. are funded by the Welsh Clinical Academic Training Fellowship. I.S.W. is the surgical Specialty Lead for Health and Care Research Wales, and reports active grants from the American Association of Plastic Surgeons and the European Association of Plastic Surgeons; is an associate editor for the Annals of Plastic Surgery, is on the editorial board of BMC Medicine, and has numerous other editorial board roles. S.R.A. received a grant from the British Association of Plastic, Reconstructive and Aesthetic Surgeons specifically for this work. The Reconstructive Surgery and Regenerative Medicine Research Centre is funded by The Scar Free Foundation and Health and Care Research Wales. The Scar Free Foundation is the only medical research charity focused on scarring with the mission to achieve scar free healing within a generation.
Issue: 9
Start Page: 1072
End Page: 1075