TY - JOUR
T1 - An umbrella review of facilitators and barriers to the adoption, spread and sustainability of AI solutions within hospital settings
AU - Clark, Sigrún Eyrúnardóttir
AU - Mathur, Sanjoli
AU - Barrado-Martín, Yolanda
AU - El-Sayed, Ahmed
AU - Zhao, Yue
AU - Li, Yiqing
AU - Khadjesari, Zarnie
AU - Stevenson, Fiona
AU - Tiwari, Manish K.
AU - Torii, Ryo
AU - Lovat, Laurence B.
AU - Vindrola-Padros, Cecilia
N1 - Data Availability Statement:
Data collected for this review will be shared upon request
PY - 2026/9/21
Y1 - 2026/9/21
N2 - There is a growing use of artificial intelligence (AI) in healthcare settings, but challenges exist with AI adoption and its long-term use. This umbrella review funded by the NIHR (NIHR205439), aimed to identify the facilitators and barriers of AI implementation within hospitals and was registered on PROSPERO. Five databases (MEDLINE, HMIC, CINAHL Plus, Web of Science and Cochrane Reviews) were searched in January 2025 and re-run in February 2026, 1184 articles were screened, with 20 included. The inclusion criteria encompassed reviews implementing AI within hospital settings. The quality of the data were assessed using the ROBIS checklist and data were synthesised using the NASSS (Nonadoption, Abandonment, and challenges to the Scale-up, Spread, and Sustainability) framework. We found AI implementation was affected by: whether an AI solution as an intervention had been externally validated to ensure generalisability across different settings; evidence that the AI solution brings measurable gains; the level of trust in and understanding of the AI solution among hospital staff; the budgets and resources available to onboard the AI solution, train staff, and maintain the solution; the need for national policies on funding and regulating AI solutions. These factors affected the adoption, spread, scalability, and sustainability of AI implementation and should be considered for future implementation.
AB - There is a growing use of artificial intelligence (AI) in healthcare settings, but challenges exist with AI adoption and its long-term use. This umbrella review funded by the NIHR (NIHR205439), aimed to identify the facilitators and barriers of AI implementation within hospitals and was registered on PROSPERO. Five databases (MEDLINE, HMIC, CINAHL Plus, Web of Science and Cochrane Reviews) were searched in January 2025 and re-run in February 2026, 1184 articles were screened, with 20 included. The inclusion criteria encompassed reviews implementing AI within hospital settings. The quality of the data were assessed using the ROBIS checklist and data were synthesised using the NASSS (Nonadoption, Abandonment, and challenges to the Scale-up, Spread, and Sustainability) framework. We found AI implementation was affected by: whether an AI solution as an intervention had been externally validated to ensure generalisability across different settings; evidence that the AI solution brings measurable gains; the level of trust in and understanding of the AI solution among hospital staff; the budgets and resources available to onboard the AI solution, train staff, and maintain the solution; the need for national policies on funding and regulating AI solutions. These factors affected the adoption, spread, scalability, and sustainability of AI implementation and should be considered for future implementation.
KW - health services
KW - systematic reviews
KW - implementation science
KW - artificial intelligence
KW - hospital
UR - https://www.scopus.com/pages/publications/105051188944
U2 - 10.1177/13558196261486714
DO - 10.1177/13558196261486714
M3 - Review article
SN - 1355-8196
JO - Journal of Health Services Research and Policy
JF - Journal of Health Services Research and Policy
ER -