Redefining healthcare roles in the age of AI: a sociotechnical perspective on professional adaptation and training

Purpose This study investigates the impact of artificial intelligence (AI) implementation on healthcare professional role adaptation and training program effectiveness, examining the mediating roles of sociotechnical integration and professional attitudes towards AI. Design/methodology/approach A cross-sectional survey was conducted among 255 healthcare professionals in India, including physicians, nurses, paramedical, and support staff from various facility types. The study employed a quantitative approach using validated scales to measure AI Technology Implementation, Sociotechnical Integration, Professional Attitudes towards AI, Professional Role Adaptation, and Training Program Effectiveness. Findings AI implementation had significant positive total effects on both professional role adaptation and training program effectiveness. These effects were partially mediated by sociotechnical integration and professional attitudes towards AI: both mediators transmitted a significant positive indirect effect from AI implementation to each outcome, while the direct effects remained positive and significant. Sociotechnical integration was the stronger mediator, particularly for training program effectiveness. The effects varied across different healthcare roles and facility types. Practical implications The findings emphasize the need for healthcare organizations to foster positive attitudes towards AI among staff, ensure thorough sociotechnical integration, and develop tailored, context-sensitive approaches to AI implementation and training programs. Originality/value This study provides a detailed, empirically grounded account of AI adoption in healthcare, showing that the benefits of AI implementation for professional roles and training are channelled through social and organizational factors rather than arising from technology alone. It highlights the critical importance of human factors and organizational context in shaping AI implementation outcomes, contributing to a more nuanced understanding of technological change in healthcare settings.