Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses' perspectives - Osama Mohamed Elsayed Ramadan; Majed Mowanes Alruwaili; Abeer Nuwayfi Alruwaili; Mohamed Gamal Elsehrawy; Sulaiman Alanazi (BMC Nursing)
Abstract Background Integrating Artificial Intelligence (AI) in nursing practice is revolutionising healthcare by enhancing clinical decision-making and patient care. However, the adoption of AI by registered nurses, especially in varied healthcare settings such as Saudi Arabia, remains underexplored. Understanding the facilitators and barriers from the perspective of frontline nurses is crucial for successful AI implementation. Aim This study aimed to explore registered nurses' perspectives on the facilitators and barriers to AI adoption in nursing practice in Saudi Arabia and to propose an extended Technology Acceptance Model for AI in Nursing (TAM-AIN). Methods A qualitative study utilising focus group discussions was conducted with 48 registered nurses from four major healthcare facilities in Al-Kharj, Saudi Arabia. Thematic analysis, guided by the Technology Acceptance Model framework, was employed to analyse the data. Results Key facilitators of AI adoption included perceived benefits to patient care (85%), strong organisational support (70%), and comprehensive training programs (75%). Primary barriers involved technical challenges (60%), ethical concerns regarding patient privacy (55%), and fears of job displacement (45%). These findings led to the development of TAM-AIN, an extended model that incorporates additional constructs such as ethical alignment, organisational readiness, and perceived threats to professional autonomy. Conclusions AI adoption in nursing practice requires a holistic approach that addresses technical, educational, ethical, and organisational challenges. The proposed TAM-AIN offers a comprehensive framework for optimising AI integration into nursing practice, emphasising the importance of nurse-centred implementation strategies. This model provides healthcare institutions and policymakers with a robust tool to facilitate successful AI adoption and enhance patient outcomes.