ArticleDigital health
Bridging trust gaps: Stakeholder perspectives on AI adoption in the United Kingdom NHS primary care.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Beyond the pilot phase: exploring the sustainable implementation of artificial intelligence in the English NHS.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study investigates the factors influencing artificial intelligence (AI) acceptance in the United Kingdom (UK) National Health Services (NHS) primary care across different stakeholder levels. It explores key barriers and enablers, focusing on workforce impact, organisational influences and ethical considerations. Methods: A mixed-methods, between-subjects approach was employed to capture stakeholder perspectives on AI trust and acceptance. Eligible participants were NHS primary care employees working at macro-, meso- and micro-levels. An online survey was distributed via Prolific and included both closed- and open-ended questions. This was followed by semi-structured interviews with a subset of volunteers. Quantitative data were analysed using descriptive statistics and qualitative responses were examined using abductive thematic analysis. Results: The study gathered 133 responses and conducted nine follow-up interviews. Three key themes were identified: (1) Workforce impact and resistance to change - concerns about job loss and skills gaps; (2) Organisational and cultural barriers - challenges around validation, leadership buy-in and integration; and (3) Trust, ethics and equity - fears about fairness, accountability and the exacerbation of healthcare disparities. Findings revealed that meso-level stakeholders (primary care managers) act as both facilitators and barriers to AI adoption, mediating between macro-level policymakers and micro-level frontline healthcare professionals while navigating organisational constraints and workforce concerns. Conclusion: The findings underscore the necessity for ethical and trustworthy AI systems, targeted stakeholder engagement, and strategies to ensure equitable and effective AI implementation in primary care. Further research should examine how trust influences AI adoption and equity in care delivery.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.