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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
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URI: https://cronfa.swan.ac.uk/Record/cronfa63002
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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. 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spelling v2 63002 2023-03-21 Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas 8c210736c07c6aa2514e0f6b3cfd9764 Stephen Ali Stephen Ali true false d18101ae0b4e72051f735ef68f45e1a8 Thomas Dobbs Thomas Dobbs true false c7bbcd83338d226f4c6157a682694a6d Matt Jovic Matt Jovic true false a6389fc6d4d18e7b67033ee04b381e43 Huw Strafford Huw Strafford true false 7d3f1e80939f2b8fab6a16b5ec6ac845 Beata Fonferko-Shadrach Beata Fonferko-Shadrach true false b69d245574e754d2637cc9e76379fe11 0000-0001-7983-8073 Arron Lacey Arron Lacey true false 1c3044b5ff7a6552ff5e8c9e3901c807 0000-0003-4396-5657 Owen Pickrell Owen Pickrell true false bdf5d5f154d339dd92bb25884b7c3652 0000-0003-4155-1741 Hayley Hutchings Hayley Hutchings true false 830074c59291938a55b480dcbee4697e Iain Whitaker Iain Whitaker true false 2023-03-21 PMSC 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. Journal Article British Journal of Surgery 110 9 1072 1075 Oxford University Press (OUP) 0007-1323 1365-2168 General surgery, plastic surgery 20 3 2023 2023-03-20 10.1093/bjs/znad055 http://dx.doi.org/10.1093/bjs/znad055 COLLEGE NANME Medicine COLLEGE CODE PMSC Swansea University SU Library paid the OA fee (TA Institutional Deal) 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. 2023-11-27T15:38:07.8622860 2023-03-21T11:44:54.0164741 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Stephen Ali 1 Thomas Dobbs 2 Matt Jovic 3 Huw Strafford 4 Beata Fonferko-Shadrach 5 Arron Lacey 0000-0001-7983-8073 6 Namor Williams 7 Owen Pickrell 0000-0003-4396-5657 8 Hayley Hutchings 0000-0003-4155-1741 9 Iain Whitaker 10 63002__27192__fc8a6177cfd5451d87fe87c4e3ad818e.pdf 63002.pdf 2023-04-25T13:21:36.9246566 Output 211681 application/pdf Version of Record true This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited true eng https://creativecommons.org/licenses/by/4.0/
title Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas
spellingShingle 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
Owen Pickrell
Hayley Hutchings
Iain Whitaker
title_short Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas
title_full Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas
title_fullStr Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas
title_full_unstemmed Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas
title_sort Validating a novel natural language processing pathway for automated quality assurance in surgical oncology: incomplete excision rates of 34 955 basal cell carcinomas
author_id_str_mv 8c210736c07c6aa2514e0f6b3cfd9764
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a6389fc6d4d18e7b67033ee04b381e43
7d3f1e80939f2b8fab6a16b5ec6ac845
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bdf5d5f154d339dd92bb25884b7c3652
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author_id_fullname_str_mv 8c210736c07c6aa2514e0f6b3cfd9764_***_Stephen Ali
d18101ae0b4e72051f735ef68f45e1a8_***_Thomas Dobbs
c7bbcd83338d226f4c6157a682694a6d_***_Matt Jovic
a6389fc6d4d18e7b67033ee04b381e43_***_Huw Strafford
7d3f1e80939f2b8fab6a16b5ec6ac845_***_Beata Fonferko-Shadrach
b69d245574e754d2637cc9e76379fe11_***_Arron Lacey
1c3044b5ff7a6552ff5e8c9e3901c807_***_Owen Pickrell
bdf5d5f154d339dd92bb25884b7c3652_***_Hayley Hutchings
830074c59291938a55b480dcbee4697e_***_Iain Whitaker
author Stephen Ali
Thomas Dobbs
Matt Jovic
Huw Strafford
Beata Fonferko-Shadrach
Arron Lacey
Owen Pickrell
Hayley Hutchings
Iain Whitaker
author2 Stephen Ali
Thomas Dobbs
Matt Jovic
Huw Strafford
Beata Fonferko-Shadrach
Arron Lacey
Namor Williams
Owen Pickrell
Hayley Hutchings
Iain Whitaker
format Journal article
container_title British Journal of Surgery
container_volume 110
container_issue 9
container_start_page 1072
publishDate 2023
institution Swansea University
issn 0007-1323
1365-2168
doi_str_mv 10.1093/bjs/znad055
publisher Oxford University Press (OUP)
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
url http://dx.doi.org/10.1093/bjs/znad055
document_store_str 1
active_str 0
description 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.
published_date 2023-03-20T15:38:08Z
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score 11.037581

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