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Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities

Ram Gurumoorthy Orcid Logo, Daniel Rees Orcid Logo, Edward Miller, Roderick Thomas

European Conference on Innovation and Entrepreneurship, Volume: 20, Issue: 1, Pages: 285 - 294

Swansea University Authors: Ram Gurumoorthy Orcid Logo, Daniel Rees Orcid Logo, Edward Miller, Roderick Thomas

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DOI (Published version): 10.34190/ecie.20.1.4200

Abstract

This study presents a narrative synthesis of the available evidence of the role of data-driven artificial intelligence (AI) in shaping social care in Wales, UK. The increasing integration of AI technologies across various sectors has raised critical questions about their potential impact on social c...

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Published in: European Conference on Innovation and Entrepreneurship
ISSN: 2049-1050 2049-1069
Published: Academic Conferences International Ltd 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70460
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Key concerns included data privacy and security, algorithm biases, the digital divide, and a lack of trust and understanding of emerging technologies among older adults. 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spelling 2025-10-30T12:50:34.6086878 v2 70460 2025-09-23 Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities 211e387999c232839bfb4508a5032973 0009-0001-1930-4050 Ram Gurumoorthy Ram Gurumoorthy true false daa6762111f9ebf62b9c2ec655512783 0000-0003-0372-6096 Daniel Rees Daniel Rees true false f78ba5e2f33f2a26d01c6e483c7c0261 Edward Miller Edward Miller true false 891091891b6eee412668ae216f713312 Roderick Thomas Roderick Thomas true false 2025-09-23 CBAE This study presents a narrative synthesis of the available evidence of the role of data-driven artificial intelligence (AI) in shaping social care in Wales, UK. The increasing integration of AI technologies across various sectors has raised critical questions about their potential impact on social care services, particularly concerning decision-making processes, ethical considerations, and the overall quality of care provided to vulnerable populations. While AI has been widely adopted in healthcare and other industries, its application within social care remains relatively underexplored with limited empirical evidence regarding its effectiveness in supporting professionals, caregivers, and service users, making it imperative to establish a solid evidence base to guide future implementation and policy decisions. Data collection involved comprehensive searches across multiple databases, including SCOPUS, PubMed, Social Care Online, and Google Scholar, alongside grey literature to ensure a thorough review of existing studies. The search strategy utilised specific keywords related to 'Artificial Intelligence' or ‘AI’, 'Social Care', and other relevant terms such as ‘data-driven’ or ‘data driven’, ‘old age’, ‘caregivers’, and ‘service users’, combined primarily using the Boolean operator 'AND,' with selective use of 'OR' to refine results, particularly around the topic of 'old age.' This focused approach yielded 642 studies, with 148 addressing AI in Social Care. Following rigorous screening, 22 studies were ultimately included, revealing substantial variability in quality, settings, and outcomes. Majority of studies focused on older people in care settings. While some studies demonstrated that AI could enhance efficiency, personalise care, and ease caregiver burdens, others highlighted significant ethical and practical obstacles. Key concerns included data privacy and security, algorithm biases, the digital divide, and a lack of trust and understanding of emerging technologies among older adults. Findings suggest AI has the potential to revolutionise social care, improving outcomes for service users, caregivers, and professionals, and highlight the need for evidence-based policymaking to integrate AI into social care and focus on long-term studies and interdisciplinary collaboration to address ethical and accessibility issues in the future Journal Article European Conference on Innovation and Entrepreneurship 20 1 285 294 Academic Conferences International Ltd 2049-1050 2049-1069 Artificial Intelligence, Social Care, Innovation, Care Delivery 19 9 2025 2025-09-19 10.34190/ecie.20.1.4200 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Another institution paid the OA fee Welsh Government (Innovation Intensive Learning Academy) 2025-10-30T12:50:34.6086878 2025-09-23T13:20:24.3185927 Faculty of Humanities and Social Sciences School of Management - Business Management Ram Gurumoorthy 0009-0001-1930-4050 1 Daniel Rees 0000-0003-0372-6096 2 Edward Miller 3 Roderick Thomas 4 70460__35504__77b1c0a11af442849959e1b7403fa4ca.pdf 70460.VoR.pdf 2025-10-30T12:48:38.8125863 Output 1088023 application/pdf Version of Record true Copyright (c) 2025 Ram Gurumoorthy, Daniel J. Rees, Edward Miller, Roderick A. Thomas. This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License. true eng https://creativecommons.org/licenses/by-nd/4.0/
title Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities
spellingShingle Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities
Ram Gurumoorthy
Daniel Rees
Edward Miller
Roderick Thomas
title_short Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities
title_full Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities
title_fullStr Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities
title_full_unstemmed Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities
title_sort Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities
author_id_str_mv 211e387999c232839bfb4508a5032973
daa6762111f9ebf62b9c2ec655512783
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author_id_fullname_str_mv 211e387999c232839bfb4508a5032973_***_Ram Gurumoorthy
daa6762111f9ebf62b9c2ec655512783_***_Daniel Rees
f78ba5e2f33f2a26d01c6e483c7c0261_***_Edward Miller
891091891b6eee412668ae216f713312_***_Roderick Thomas
author Ram Gurumoorthy
Daniel Rees
Edward Miller
Roderick Thomas
author2 Ram Gurumoorthy
Daniel Rees
Edward Miller
Roderick Thomas
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institution Swansea University
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publisher Academic Conferences International Ltd
college_str Faculty of Humanities and Social Sciences
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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 Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
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description This study presents a narrative synthesis of the available evidence of the role of data-driven artificial intelligence (AI) in shaping social care in Wales, UK. The increasing integration of AI technologies across various sectors has raised critical questions about their potential impact on social care services, particularly concerning decision-making processes, ethical considerations, and the overall quality of care provided to vulnerable populations. While AI has been widely adopted in healthcare and other industries, its application within social care remains relatively underexplored with limited empirical evidence regarding its effectiveness in supporting professionals, caregivers, and service users, making it imperative to establish a solid evidence base to guide future implementation and policy decisions. Data collection involved comprehensive searches across multiple databases, including SCOPUS, PubMed, Social Care Online, and Google Scholar, alongside grey literature to ensure a thorough review of existing studies. The search strategy utilised specific keywords related to 'Artificial Intelligence' or ‘AI’, 'Social Care', and other relevant terms such as ‘data-driven’ or ‘data driven’, ‘old age’, ‘caregivers’, and ‘service users’, combined primarily using the Boolean operator 'AND,' with selective use of 'OR' to refine results, particularly around the topic of 'old age.' This focused approach yielded 642 studies, with 148 addressing AI in Social Care. Following rigorous screening, 22 studies were ultimately included, revealing substantial variability in quality, settings, and outcomes. Majority of studies focused on older people in care settings. While some studies demonstrated that AI could enhance efficiency, personalise care, and ease caregiver burdens, others highlighted significant ethical and practical obstacles. Key concerns included data privacy and security, algorithm biases, the digital divide, and a lack of trust and understanding of emerging technologies among older adults. Findings suggest AI has the potential to revolutionise social care, improving outcomes for service users, caregivers, and professionals, and highlight the need for evidence-based policymaking to integrate AI into social care and focus on long-term studies and interdisciplinary collaboration to address ethical and accessibility issues in the future
published_date 2025-09-19T05:31:33Z
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