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Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom

Marty Chamberlain Orcid Logo

Swansea University Author: Marty Chamberlain Orcid Logo

Abstract

This horizon scanning Wellcome Trust funded project was tasked with exploring the impact of currentadvances in computing and information processing, in the field of professional regulation in the UnitedKingdom.Although key advances in mathematics, information processing, machine learning, automation...

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Published: Swansea 2019
URI: https://cronfa.swan.ac.uk/Record/cronfa48862
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first_indexed 2019-02-14T20:03:07Z
last_indexed 2019-08-22T15:23:53Z
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spelling 2019-08-22T10:54:35.8263610 v2 48862 2019-02-14 Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom 98bbc13e72a7ce4126a562a668e50144 0000-0001-6067-6561 Marty Chamberlain Marty Chamberlain true false 2019-02-14 CRIM This horizon scanning Wellcome Trust funded project was tasked with exploring the impact of currentadvances in computing and information processing, in the field of professional regulation in the UnitedKingdom.Although key advances in mathematics, information processing, machine learning, automation andartificial intelligence are beginning to disrupt and transform traditional practices in health and social carein the United Kingdom, the project found that the same cannot be said in relation to the field of professionalregulation.At present, the focus of the regulatory reform agenda has been on promoting a more joined-up, risk-adverseand public-interest focused model of ‘right touch’ regulation. However, the project concluded that thisagenda will not by itself enable regulators to embed current and future developments in automation andmachine learning within their organisational structures.The fractured and decontextualized nature of the current regulatory data lake means that despite their recentefforts to develop their respective intelligence and insight agendas to improve the predictive risk templatesused to identify threats to public safety, at present regulators possess a very low level of readiness in relationto the information capture and analysis systems required by algorithmic digital technologies.It is the key recommendation of the project that action be taken to standardize current regulatory datawarehouse information capture and processing systems, with a view to support the development of a shareddata lake between regulators. Furthermore, this warehouse should be curated by an independent statutorybody, such as the Professional Standards Authority, to meet public-interest expectations and GDPRrequirements, particularly in relation to the future development of regulatory predictive risk-templates. ResearchReportExternalBody Swansea Regulation, Computing, Information Processing, Big Data 15 2 2019 2019-02-15 Briefing Report - Project funded by the Wellcome Trust COLLEGE NANME Criminology COLLEGE CODE CRIM Swansea University 2019-08-22T10:54:35.8263610 2019-02-14T17:40:33.4201174 Faculty of Humanities and Social Sciences School of Social Sciences - Criminology, Sociology and Social Policy Marty Chamberlain 0000-0001-6067-6561 1 0048862-14022019174500.pdf 48862.WellcomeTrustSwanseaReport.pdf 2019-02-14T17:45:00.5600000 Output 324487 application/pdf Version of Record true 2019-02-14T00:00:00.0000000 true eng
title Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom
spellingShingle Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom
Marty Chamberlain
title_short Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom
title_full Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom
title_fullStr Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom
title_full_unstemmed Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom
title_sort Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom
author_id_str_mv 98bbc13e72a7ce4126a562a668e50144
author_id_fullname_str_mv 98bbc13e72a7ce4126a562a668e50144_***_Marty Chamberlain
author Marty Chamberlain
author2 Marty Chamberlain
format ResearchReportExternalBody
publishDate 2019
institution Swansea University
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Social Sciences - Criminology, Sociology and Social Policy{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Social Sciences - Criminology, Sociology and Social Policy
document_store_str 1
active_str 0
description This horizon scanning Wellcome Trust funded project was tasked with exploring the impact of currentadvances in computing and information processing, in the field of professional regulation in the UnitedKingdom.Although key advances in mathematics, information processing, machine learning, automation andartificial intelligence are beginning to disrupt and transform traditional practices in health and social carein the United Kingdom, the project found that the same cannot be said in relation to the field of professionalregulation.At present, the focus of the regulatory reform agenda has been on promoting a more joined-up, risk-adverseand public-interest focused model of ‘right touch’ regulation. However, the project concluded that thisagenda will not by itself enable regulators to embed current and future developments in automation andmachine learning within their organisational structures.The fractured and decontextualized nature of the current regulatory data lake means that despite their recentefforts to develop their respective intelligence and insight agendas to improve the predictive risk templatesused to identify threats to public safety, at present regulators possess a very low level of readiness in relationto the information capture and analysis systems required by algorithmic digital technologies.It is the key recommendation of the project that action be taken to standardize current regulatory datawarehouse information capture and processing systems, with a view to support the development of a shareddata lake between regulators. Furthermore, this warehouse should be curated by an independent statutorybody, such as the Professional Standards Authority, to meet public-interest expectations and GDPRrequirements, particularly in relation to the future development of regulatory predictive risk-templates.
published_date 2019-02-15T03:59:32Z
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score 11.036706

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