The Relevance of Trust in the Implementation of AI-Driven Clinical Decision Support Systems by Healthcare Professionals: An Extended UTAUT Model

Background. In the healthcare sector clinical decision support powered by artificial intelligence is rapidly expanding. It can help medical personnel make better-informed decisions while saving time. The opposition of healthcare professionals to AI initiatives in the healthcare industry might prove to be a significant barrier, due to the combination of optimism and fear related to the technology. Professionals in the sector may be apprehensive about AI due to job security worries and the general public's lack of faith in the technology. Objective. A primary objective of this study is to examine how trust influences healthcare professionals' willingness to use AI-driven CDSS, particularly at well-known tertiary hospitals in Delhi-NCR region India, as well as to expand the Unified theory of acceptance and use of technology (UTAUT) by including constructs such as perceived risk and self-efficacy. Method. The UTAUT model provided the basis for the construction of a new model, which was developed by integrating the variables of perceived risk and self-efficacy. The model had eight components which were assessed using 31 survey questions. Two hundred and twenty participants filled out the surveys for the study. Results. The findings indicate that there are significant relationships between (PE) and (AI), (EE) and(AI), (PR) and (AI) and (SE) and (AI). This study reveals that trust fully mediates the relationship between AI which means that fostering trust in AI technology is essential for the successful adoption and implementation of AI-CDSS technology. There was no significant relationship between social influence and adoption of AI-powered CDSS nor between intention to adopt and actual use. Conclusion. The result of this study shed light on the factors influencing healthcare professionals to adopt AI-CDSS. The outcomes of this study provide insight into how individuals' performance expectancy, effort expectancy, social influence, perceived risk, and self-efficacy determine whether they embrace AI-CDSS. This study highlights the importance of trust in the deployment of these technologies in the healthcare sector. Furthermore, it highlights the significance of addressing perceived risk and self-efficacy. Although social influence plays a crucial role in technology adoption its impact may be limited in the case of AI-CDSS deployment in India. This could be due to the complexities of AI-CDSS implementation, the requirement for training, or the fact that healthcare personnel are unaware of the benefits of AI-CDSS or have limited exposure to it.