Factors influencing doctors’ acceptance of artificial intelligence-enabled clinical decision support systems in tertiary hospitals in China

Abstract

Abstract Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) can enhance decision-making ability to improve healthcare performance and efficiency by introducing new AI technologies such as deep neural networks and knowledge graphs. This study aimed to analyze the factors influencing doctors’ acceptance of AI-CDSSs in tertiary hospitals in China. We proposed a hypothesized model based on the unified theory of acceptance and use of technology (UTAUT) model, and developed a 25-item measurement scale. 187 doctors completed the web-survey, who’s from four tertiary hospitals in four provinces in China. We used partial least squares structural equation modeling (PLS-SEM) to analyze the data. The Cronbach α value was 0.932, the corrected item to total correlation values ranged from 0.467 to 0.744, and the average variance of extracted values ranged from 0.628 to 0.782. The standardized root mean square residual value was 0.055, the squared Euclidean distance and geodesic distance were 0.995 and 0.566, respectively. The model had explained 73.1% of doctors’ acceptance. Doctors’ acceptance of AI-CDSSs is strongly influenced by effort expectancy, is moderately influenced by trust in AI and is weakly influenced by social factors. Trust in AI as an important new factor is moderately influenced by social factors and is weakly influenced by effort expectancy and performance expectancy. Compared to the performance of decision-making, doctors are more interested in improving efficiency. Moreover, improving the ease of use, focusing on doctors' trust in AI technology, and enhancing the introduction of AI-CDSSs will encourage doctors to accept AI-CDSSs.