Drivers of artificial intelligence integrated healthcare applications. Patients' perspective

The healthcare sector has extensively adopted Artificial Intelligence (AI) to improve operational efficiency and care quality. However, patients' adoption and use of AI-integrated healthcare applications (AIIHA) for consultation and treatment have not been investigated sufficiently. Our study addresses this gap by examining a unique contextual model comprising antecedents of patients' intentions and behavior towards AIIHA. The proposed model is contextualized to the AIIHA context by incorporating four new constructs, namely privacy concern, hedonic motivation, trust, and patient healthcare engagement, along with performance and effort expectancy. The model hypothesizing the association of the aforementioned variables with patients' behavioral intentions is tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data (N = 522) collected online from respondents in different countries. In addition, the moderating effect of the recent pandemic is also tested to evaluate the strength of the association between behavioral intentions and usage behavior towards AIIHA. The results reveal that performance expectancy, effort expectancy, hedonic motivation, patient healthcare engagement, and trust shape patients' behavioral intentions towards AIIHA. Also, there is a statistically significant effect of effort expectancy on performance expectancy, patients' behavioral intentions on usage behavior, and the negative impact of privacy concerns on patients' trust.