A mixed methods formative evaluation of the United Kingdom National Health Service Artificial Intelligence Lab
Abstract Internationally, health systems are investing in Artificial Intelligence (AI) to improve safety, quality, and efficiency, yet many efforts remain localised and do not progress beyond early development stages. In 2019, National Health Service (NHS) England and the Department of Health and So...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01805-w |
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| author | Kathrin Cresswell Robin Williams Sheena Dungey Stuart Anderson Miguel O. Bernabeu Hajar Mozaffar Xiao Yang Varun Sai Sara Bea Sally Eason |
| author_facet | Kathrin Cresswell Robin Williams Sheena Dungey Stuart Anderson Miguel O. Bernabeu Hajar Mozaffar Xiao Yang Varun Sai Sara Bea Sally Eason |
| author_sort | Kathrin Cresswell |
| collection | DOAJ |
| description | Abstract Internationally, health systems are investing in Artificial Intelligence (AI) to improve safety, quality, and efficiency, yet many efforts remain localised and do not progress beyond early development stages. In 2019, National Health Service (NHS) England and the Department of Health and Social Care launched the AI Lab to accelerate safe AI adoption. We conducted a mixed-methods evaluation of the AI Lab, analysing 1021 documents and 85 stakeholder interviews. The AI Lab made important contributions to national AI policy, regulation, and capability building, and positioned the United Kingdom as a global leader in AI deployment for health. Despite progress, implementation and scaling were hindered by shifting objectives, limited capacity, and systemic misalignment with service needs. Some AI technologies demonstrated high return on investment and improved clinical processes. Lessons from the AI Lab highlight critical socio-organisational factors, gaps in scaling support, and the need for sustained coordination to realise the long-term benefits of AI in health and social care systems. |
| format | Article |
| id | doaj-art-9b04556a6c204c4b832db25b4ec7bd31 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-9b04556a6c204c4b832db25b4ec7bd312025-08-20T04:03:11ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111910.1038/s41746-025-01805-wA mixed methods formative evaluation of the United Kingdom National Health Service Artificial Intelligence LabKathrin Cresswell0Robin Williams1Sheena Dungey2Stuart Anderson3Miguel O. Bernabeu4Hajar Mozaffar5Xiao Yang6Varun Sai7Sara Bea8Sally Eason9Usher Institute, University of EdinburghInstitute for the Study of Science, Technology and Innovation, University of EdinburghNHS Arden GEM Commissioning Support UnitSchool of Informatics, University of EdinburghUsher Institute, University of EdinburghBusiness School, University of EdinburghInstitute for the Study of Science, Technology and Innovation, University of EdinburghUsher Institute, University of EdinburghUsher Institute, University of EdinburghNHS Arden GEM Commissioning Support UnitAbstract Internationally, health systems are investing in Artificial Intelligence (AI) to improve safety, quality, and efficiency, yet many efforts remain localised and do not progress beyond early development stages. In 2019, National Health Service (NHS) England and the Department of Health and Social Care launched the AI Lab to accelerate safe AI adoption. We conducted a mixed-methods evaluation of the AI Lab, analysing 1021 documents and 85 stakeholder interviews. The AI Lab made important contributions to national AI policy, regulation, and capability building, and positioned the United Kingdom as a global leader in AI deployment for health. Despite progress, implementation and scaling were hindered by shifting objectives, limited capacity, and systemic misalignment with service needs. Some AI technologies demonstrated high return on investment and improved clinical processes. Lessons from the AI Lab highlight critical socio-organisational factors, gaps in scaling support, and the need for sustained coordination to realise the long-term benefits of AI in health and social care systems.https://doi.org/10.1038/s41746-025-01805-w |
| spellingShingle | Kathrin Cresswell Robin Williams Sheena Dungey Stuart Anderson Miguel O. Bernabeu Hajar Mozaffar Xiao Yang Varun Sai Sara Bea Sally Eason A mixed methods formative evaluation of the United Kingdom National Health Service Artificial Intelligence Lab npj Digital Medicine |
| title | A mixed methods formative evaluation of the United Kingdom National Health Service Artificial Intelligence Lab |
| title_full | A mixed methods formative evaluation of the United Kingdom National Health Service Artificial Intelligence Lab |
| title_fullStr | A mixed methods formative evaluation of the United Kingdom National Health Service Artificial Intelligence Lab |
| title_full_unstemmed | A mixed methods formative evaluation of the United Kingdom National Health Service Artificial Intelligence Lab |
| title_short | A mixed methods formative evaluation of the United Kingdom National Health Service Artificial Intelligence Lab |
| title_sort | mixed methods formative evaluation of the united kingdom national health service artificial intelligence lab |
| url | https://doi.org/10.1038/s41746-025-01805-w |
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