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Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses’ perspectives
Citation. Ramadan, O. M. E., Alruwaili, M. M., Alruwaili, A. N., Elsehrawy, M. G., & Alanazi, S. (2024). Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses’ perspectives. BMC Nursing, 23, 891. https://doi.org/10.1186/s12912-024-02571-y
Summary. This qualitative focus-group study examines how 48 registered nurses in four healthcare facilities in Al-Kharj, Saudi Arabia understand AI and describe its adoption conditions. Participants saw potential patient-care benefits but also described organizational, technical, ethical, professional, and training-related constraints. The authors propose an extended Technology Acceptance Model for AI in Nursing (TAM-AIN) that adds ethical alignment, organizational readiness, professional-identity preservation, and technical-infrastructure capacity.
Key claims
- Perceived patient-care benefits, organizational support and leadership commitment, workflow integration, and education and training emerged as adoption facilitators in the focus-group analysis.
- Participants identified technical and resource limitations, privacy and ethical concerns, fear of job displacement or deskilling, and resistance or skepticism as barriers to adoption.
- In the reported sample, 70% emphasized organizational support, while 60% cited technical challenges and infrastructure limitations; the study reports 48 participants across four facilities.
- The proposed TAM-AIN extends the Technology Acceptance Model with nursing-specific attention to ethical alignment, organizational readiness, professional identity, and technical infrastructure.
Evidence & limitations
- Qualitative thematic analysis of focus-group discussions with 48 registered nurses in four healthcare facilities in Al-Kharj, Saudi Arabia; the reported percentages describe this sample’s responses rather than population estimates.
- The authors note the geographically bounded sample and cross-sectional design, so transferability across settings and change over time remain uncertain.