Effect of risk, expectancy, and trust on clinicians’ intent to use an artificial intelligence system -- Blood Utilization Calculator

Abstract

A gap exists between the capabilities of artificial intelligence (AI) technologies in healthcare and the extent to which clinicians are willing to adopt these systems. Our study addressed this gap by leveraging ‘expectancy-value theory’ and ‘modified extended unified theory of acceptance and use of technology’ to understand why clinicians may be willing or unwilling to adopt AI systems. The study looked at the ‘expectancy,’ ‘trust,’ and ‘perceptions’ of clinicians related to their intention of using an AI-based decision support system known as the Blood Utilization Calculator (BUC). The study used purposive sampling to recruit BUC users and administered a validated online survey from a large hospital system in the Midwest in 2021. The findings captured the significant effect of ‘perceived risk’ (negatively) and ‘expectancy’ (positively) on clinicians' ‘trust’ in BUC. ‘Trust’ was also found to mediate the relationship of ‘perceived risk’ and ‘expectancy’ with the ‘intent to use BUC.’ The study's findings established pathways for future research and have implications on factors influencing BUC use.