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Data-Driven AI in Social Care Wales: Identifying Gaps and Opportunities
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Daniel Rees ,
Edward Miller,
Roderick Thomas
European Conference on Innovation and Entrepreneurship, Volume: 20, Issue: 1, Pages: 285 - 294
Swansea University Authors:
Ram Gurumoorthy , Daniel Rees , Edward Miller, Roderick Thomas
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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.
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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...
| Published in: | European Conference on Innovation and Entrepreneurship |
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| ISSN: | 2049-1050 2049-1069 |
| Published: |
Academic Conferences International Ltd
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70460 |
| 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 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 |
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| Keywords: |
Artificial Intelligence, Social Care, Innovation, Care Delivery |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Welsh Government (Innovation Intensive Learning Academy) |
| Issue: |
1 |
| Start Page: |
285 |
| End Page: |
294 |