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Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings

Stephen Ali, Thomas Dobbs, Adib Tarafdar, Huw Strafford, Beata Fonferko-Shadrach, Arron S. Lacey Orcid Logo, Owen Pickrell Orcid Logo, Hayley Hutchings Orcid Logo, Iain Whitaker

British Journal of Surgery, Volume: 111, Issue: 1

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

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DOI (Published version): 10.1093/bjs/znad347

Abstract

BackgroundCancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate...

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Published in: British Journal of Surgery
ISSN: 0007-1323 1365-2168
Published: Oxford University Press (OUP) 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65543
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Abstract: BackgroundCancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate evidence-based MDT decisions for skin cancer with basal cell carcinoma as a use case.MethodsA novel and validated NLP information extraction model was used to extract perioperative tumour and surgical factors from histopathology reports. A web application with a bespoke application programming interface used data from this model to provide an automated clinical decision support system, mapped to national guidelines and generating a patient letter to communicate ongoing management. Performance was assessed against retrospectively derived recommendations by two independent and blinded expert clinicians.ResultsThere were 893 patients (1045 lesions) used to internally validate the model. High accuracy was observed when compared against human predictions, with an overall value of 0.92. Across all classifiers the virtual skin MDT was highly specific (0.96), while sensitivity was lower (0.72).ConclusionThis study demonstrates the feasibility of a fully automated, virtual, web-based service model to host the skin MDT with good system performance. This platform could be used to support clinical decision-making during MDTs as ‘human in the loop’ approach to aid protocolized streaming. Future prospective studies are needed to validate the model in tumour types where guidelines are more complex.
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, editorial board of BMC Medicine and 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 & 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: 1