Sentiment of Nurses Towards Artificial Intelligence and Resistance to Change in Healthcare Organisations: A Mixed-Method Study

Background: Research identified preliminary evidence that artificial intelligence (AI) has emerged as a transformative force in healthcare, revolutionising various aspects of healthcare delivery, from diagnostics to treatment planning. However, integrating AI into healthcare systems in Egypt is challenging, particularly concerning healthcare professionals' acceptance and adoption of these technologies. This mixed-method study aimed to explore the sentiment of nurses at different organisational levels towards AI and resistance to change in healthcare organisations. Methods: A mixed-method design was employed, with quantitative data collected through a survey of 500 nurses using the general attitudes towards AI and resistance to change scale and qualitative data from semi-structured interviews with 17 nurses. Quantitative data were analysed using descriptive and inferential statistics, while qualitative data were analysed thematically. Results: The survey demonstrated that positive attitudes were inversely correlated with resistance behaviour and resistance to change. Additionally, perceptions of AI's usefulness, ease of use and value were strongly and positively correlated with positive attitudes and negatively correlated with negative attitudes. Moreover, the influence of colleagues' opinions, self-efficacy for change and organisational support showed significant positive correlations with positive attitudes towards AI and negative correlations with negative attitudes. Qualitatively, nurses cited obstacles such as lack of familiarity with AI technologies, biases affecting decision-making, technological challenges, inadequate training and fear of technology replacing human interaction. Readiness for AI integration was associated with the necessity of training and the timing of AI use. Conclusion: Nurses demonstrated varied understanding of AI's applications and benefits. Some acknowledged its potential for efficiency and time-saving, while others highlighted a need for up-to-date knowledge. Patient or Public Contribution: No patient or public contribution.