Barriers and facilitators to healthcare artificial intelligence adoption in Wales: findings from focus groups
- 1School of Health Sciences, University of Manchester , ,
- 2CEDAR, Cardiff and Vale University Health Board , ,
Abstract
Background
Building on a previous Wales-wide survey, this study aimed to provide more in-depth exploration of the barriers and facilitators to healthcare artificial intelligence adoption in Wales.Objectives
The study aimed to explore these barriers and facilitators among various social groups, to inform policy development in Wales.Methods
Four focus groups were conducted with a group of members of the public and a group of healthcare professionals (both groups recruited from a previous survey), as well as a group Sixth Form students and a group of retired adults. Inductive thematic analysis was used to generate themes and to compare the issues raised by the various groups. Contiguity-based analysis was undertaken to underscore how themes related with each other.Results
A total of 21 individuals participated in the focus groups (healthcare professionals=6, members of the public=6, sixth-form students=5, retirees=4). Seven thematic networks were identified. Across groups, seven themes were identified in the healthcare professional group, six in the general members of the public group, seven in the sixth form student group, and seven in the retiree group. All groups voiced concerns about data privacy and highlighted the importance of human oversight of AI and human contact during the clinical encounter as they expressed more trust in human practitioners. There were though differences between groups, such as greater trust in black-box systems by healthcare professionals, and between individual participants in regard to trust in private organisations developing AI for healthcare organisations.Conclusions
All groups were concerned with information security and expressed a preference for human contact during the clinical encounter and a human-in-the-loop approach. There were differences between the groups, such as in attitudes towards black-box AI technologies and trust in clinicians. No additional barriers or facilitators were identified for proxy consent.
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BibTeX
@article{pruski2026barriers,
title = {{Barriers and facilitators to healthcare artificial intelligence adoption in Wales: findings from focus groups}},
author = {Michal Pruski and Jasmine Rollings and Kathleen L. Withers},
journal = {Frontiers in digital health},
year = {2026},
month = oct,
doi = {10.3389/fdgth.2026.1927799},
url = {https://europepmc.org/article/PMC/PMC13717693},
note = {CC BY},
}
RIS
TY - JOUR
TI - Barriers and facilitators to healthcare artificial intelligence adoption in Wales: findings from focus groups
AU - Michal Pruski
AU - Jasmine Rollings
AU - Kathleen L. Withers
PY - 2026
DA - 2026/10/12
JO - Frontiers in digital health
DO - 10.3389/fdgth.2026.1927799
UR - https://europepmc.org/article/PMC/PMC13717693
AB - Background Building on a previous Wales-wide survey, this study aimed to provide more in-depth exploration of the barriers and facilitators to healthcare artificial intelligence adoption in Wales.Objectives The study aimed to explore these barriers and facilitators among various social groups, to inform policy development in Wales.Methods Four focus groups were conducted with a group of members of the public and a group of healthcare professionals (both groups recruited from a previous survey), as well as a group Sixth Form students and a group of retired adults. Inductive thematic analysis was used to generate themes and to compare the issues raised by the various groups. Contiguity-based analysis was undertaken to underscore how themes related with each other.Results A total of 21 individuals participated in the focus groups (healthcare professionals=6, members of the public=6, sixth-form students=5, retirees=4). Seven thematic networks were identified. Across groups, seven themes were identified in the healthcare professional group, six in the general members of the public group, seven in the sixth form student group, and seven in the retiree group. All groups voiced concerns about data privacy and highlighted the importance of human oversight of AI and human contact during the clinical encounter as they expressed more trust in human practitioners. There were though differences between groups, such as greater trust in black-box systems by healthcare professionals, and between individual participants in regard to trust in private organisations developing AI for healthcare organisations.Conclusions All groups were concerned with information security and expressed a preference for human contact during the clinical encounter and a human-in-the-loop approach. There were differences between the groups, such as in attitudes towards black-box AI technologies and trust in clinicians. No additional barriers or facilitators were identified for proxy consent.
SN - 2673-253X
ER -