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Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe

MANUELA ROMAN, Stephen Ali, Nader Ibrahim, Thomas Dobbs, Hayley Hutchings Orcid Logo, Iain Whitaker

Health Information Management Journal, Volume: 55, Issue: 1, Pages: 123 - 131

Swansea University Authors: MANUELA ROMAN, Stephen Ali, Thomas Dobbs, Hayley Hutchings Orcid Logo, Iain Whitaker

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DOI (Published version): 10.1177/18333583251378962

Abstract

Background: Automated clinical coding can use statistical or artificial intelligence-based technology to transform unstructured clinical data into clinical codes. These processes have the potential to enhance the quality and accuracy of data collections, save resources and accelerate research. Objec...

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Published in: Health Information Management Journal
ISSN: 1833-3583 1833-3575
Published: SAGE Publications 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71234
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These processes have the potential to enhance the quality and accuracy of data collections, save resources and accelerate research. Objective: To evaluate the use of automated clinical coding in the United Kingdom (UK) and European cancer registries. Method: An online electronic survey was formulated to evaluate the use and user opinion of automation within cancer registries. The survey was distributed to members of the United Kingdom and Ireland Association of Cancer Registry and the European cancer registries. Data analysis was performed using Microsoft Excel 2015 &#xAE; version 15.13.3 in order to summarise the results. Results: Twenty-three of the 117 cancer registries responded to the distributed survey; 15 (12.8%) cancer registries used automation within their registry, mainly in the form of natural language processing or machine learning. Most of the sampled registries (73.3%) used these technologies to automate data collection from pathology reports; 87% of respondents reported automation as efficient; and 26.1% reported improved data quality; 12 (52.1%) of cancer registries still manually checked all the automations; and 17 (74%) respondents believed that the algorithms for difficult tasks require further development. Conclusion: Various computer-based algorithms have been used for automated clinical coding in the UK and European cancer registries in the past few decades; however, to date there are no published data to validate its use. Further research and development of these technologies is needed to ensure external validity and maximise the potential use within other cancer registries globally. Implications for health information management practice: It is clear that while automation can be advantageous in areas of clinical coding, the role of the &#x201C;human&#x201D; (HIMs and clinical coders) in coding and classifying registry data, and in overseeing the transition, will be required for some time yet.</abstract><type>Journal Article</type><journal>Health Information Management Journal</journal><volume>55</volume><journalNumber>1</journalNumber><paginationStart>123</paginationStart><paginationEnd>131</paginationEnd><publisher>SAGE Publications</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1833-3583</issnPrint><issnElectronic>1833-3575</issnElectronic><keywords>registries; registry data; natural language processing; research; clinical coding; medical record system; automated; health information management; cancer registries; computer assisted coding; automated coding</keywords><publishedDay>1</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2026</publishedYear><publishedDate>2026-01-01</publishedDate><doi>10.1177/18333583251378962</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm>Other</apcterm><funders/><projectreference/><lastEdited>2026-01-13T11:22:19.7907008</lastEdited><Created>2026-01-13T11:09:39.6718371</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Biomedical Science</level></path><authors><author><firstname>MANUELA</firstname><surname>ROMAN</surname><order>1</order></author><author><firstname>Stephen</firstname><surname>Ali</surname><order>2</order></author><author><firstname>Nader</firstname><surname>Ibrahim</surname><order>3</order></author><author><firstname>Thomas</firstname><surname>Dobbs</surname><order>4</order></author><author><firstname>Hayley</firstname><surname>Hutchings</surname><orcid>0000-0003-4155-1741</orcid><order>5</order></author><author><firstname>Iain</firstname><surname>Whitaker</surname><orcid/><order>6</order></author></authors><documents><document><filename>71234__35977__f160d54085cf4b8c8412f035cc6504d7.pdf</filename><originalFilename>71234.VOR.pdf</originalFilename><uploaded>2026-01-13T11:18:54.3604684</uploaded><type>Output</type><contentLength>661202</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; The Author(s) 2025. 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spelling 2026-01-13T11:22:19.7907008 v2 71234 2026-01-13 Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe c916da20ff4d0ae82dbb7b21d9ed9cb6 MANUELA ROMAN MANUELA ROMAN true false 8c210736c07c6aa2514e0f6b3cfd9764 Stephen Ali Stephen Ali true false d18101ae0b4e72051f735ef68f45e1a8 Thomas Dobbs Thomas Dobbs true false bdf5d5f154d339dd92bb25884b7c3652 0000-0003-4155-1741 Hayley Hutchings Hayley Hutchings true false 830074c59291938a55b480dcbee4697e Iain Whitaker Iain Whitaker true false 2026-01-13 Background: Automated clinical coding can use statistical or artificial intelligence-based technology to transform unstructured clinical data into clinical codes. These processes have the potential to enhance the quality and accuracy of data collections, save resources and accelerate research. Objective: To evaluate the use of automated clinical coding in the United Kingdom (UK) and European cancer registries. Method: An online electronic survey was formulated to evaluate the use and user opinion of automation within cancer registries. The survey was distributed to members of the United Kingdom and Ireland Association of Cancer Registry and the European cancer registries. Data analysis was performed using Microsoft Excel 2015 ® version 15.13.3 in order to summarise the results. Results: Twenty-three of the 117 cancer registries responded to the distributed survey; 15 (12.8%) cancer registries used automation within their registry, mainly in the form of natural language processing or machine learning. Most of the sampled registries (73.3%) used these technologies to automate data collection from pathology reports; 87% of respondents reported automation as efficient; and 26.1% reported improved data quality; 12 (52.1%) of cancer registries still manually checked all the automations; and 17 (74%) respondents believed that the algorithms for difficult tasks require further development. Conclusion: Various computer-based algorithms have been used for automated clinical coding in the UK and European cancer registries in the past few decades; however, to date there are no published data to validate its use. Further research and development of these technologies is needed to ensure external validity and maximise the potential use within other cancer registries globally. Implications for health information management practice: It is clear that while automation can be advantageous in areas of clinical coding, the role of the “human” (HIMs and clinical coders) in coding and classifying registry data, and in overseeing the transition, will be required for some time yet. Journal Article Health Information Management Journal 55 1 123 131 SAGE Publications 1833-3583 1833-3575 registries; registry data; natural language processing; research; clinical coding; medical record system; automated; health information management; cancer registries; computer assisted coding; automated coding 1 1 2026 2026-01-01 10.1177/18333583251378962 COLLEGE NANME COLLEGE CODE Swansea University Other 2026-01-13T11:22:19.7907008 2026-01-13T11:09:39.6718371 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Biomedical Science MANUELA ROMAN 1 Stephen Ali 2 Nader Ibrahim 3 Thomas Dobbs 4 Hayley Hutchings 0000-0003-4155-1741 5 Iain Whitaker 6 71234__35977__f160d54085cf4b8c8412f035cc6504d7.pdf 71234.VOR.pdf 2026-01-13T11:18:54.3604684 Output 661202 application/pdf Version of Record true © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License. true eng https://creativecommons.org/licenses/by/4.0/
title Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe
spellingShingle Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe
MANUELA ROMAN
Stephen Ali
Thomas Dobbs
Hayley Hutchings
Iain Whitaker
title_short Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe
title_full Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe
title_fullStr Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe
title_full_unstemmed Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe
title_sort Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe
author_id_str_mv c916da20ff4d0ae82dbb7b21d9ed9cb6
8c210736c07c6aa2514e0f6b3cfd9764
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bdf5d5f154d339dd92bb25884b7c3652
830074c59291938a55b480dcbee4697e
author_id_fullname_str_mv c916da20ff4d0ae82dbb7b21d9ed9cb6_***_MANUELA ROMAN
8c210736c07c6aa2514e0f6b3cfd9764_***_Stephen Ali
d18101ae0b4e72051f735ef68f45e1a8_***_Thomas Dobbs
bdf5d5f154d339dd92bb25884b7c3652_***_Hayley Hutchings
830074c59291938a55b480dcbee4697e_***_Iain Whitaker
author MANUELA ROMAN
Stephen Ali
Thomas Dobbs
Hayley Hutchings
Iain Whitaker
author2 MANUELA ROMAN
Stephen Ali
Nader Ibrahim
Thomas Dobbs
Hayley Hutchings
Iain Whitaker
format Journal article
container_title Health Information Management Journal
container_volume 55
container_issue 1
container_start_page 123
publishDate 2026
institution Swansea University
issn 1833-3583
1833-3575
doi_str_mv 10.1177/18333583251378962
publisher SAGE Publications
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 - Biomedical Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Biomedical Science
document_store_str 1
active_str 0
description Background: Automated clinical coding can use statistical or artificial intelligence-based technology to transform unstructured clinical data into clinical codes. These processes have the potential to enhance the quality and accuracy of data collections, save resources and accelerate research. Objective: To evaluate the use of automated clinical coding in the United Kingdom (UK) and European cancer registries. Method: An online electronic survey was formulated to evaluate the use and user opinion of automation within cancer registries. The survey was distributed to members of the United Kingdom and Ireland Association of Cancer Registry and the European cancer registries. Data analysis was performed using Microsoft Excel 2015 ® version 15.13.3 in order to summarise the results. Results: Twenty-three of the 117 cancer registries responded to the distributed survey; 15 (12.8%) cancer registries used automation within their registry, mainly in the form of natural language processing or machine learning. Most of the sampled registries (73.3%) used these technologies to automate data collection from pathology reports; 87% of respondents reported automation as efficient; and 26.1% reported improved data quality; 12 (52.1%) of cancer registries still manually checked all the automations; and 17 (74%) respondents believed that the algorithms for difficult tasks require further development. Conclusion: Various computer-based algorithms have been used for automated clinical coding in the UK and European cancer registries in the past few decades; however, to date there are no published data to validate its use. Further research and development of these technologies is needed to ensure external validity and maximise the potential use within other cancer registries globally. Implications for health information management practice: It is clear that while automation can be advantageous in areas of clinical coding, the role of the “human” (HIMs and clinical coders) in coding and classifying registry data, and in overseeing the transition, will be required for some time yet.
published_date 2026-01-01T05:34:56Z
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