Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology (Preprint)

Abstract

BACKGROUND Artificial Intelligence-enabled Clinical Decision Support Systems (AI-CDSSs) offer potential for improving healthcare outcomes, but their adoption among healthcare practitioners remains limited. OBJECTIVE The meta-analysis identifies predictors influencing healthcare practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and additional literature. METHODS The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7,731 results, of which 17 studies met the inclusion criteria. Random-effects meta-analysis, relative weights analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships of relevant predictor variables with the intention to use AI-CDSSs. RESULTS The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results show that performance expectancy (rc = .66), effort expectancy (rc = .55), social influence (rc = .66), and facilitating conditions (rc = .66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (rc = .63), trust (rc = .73), anxiety (rc = -.41), perceived risk (rc = -.21), and innovativeness (rc = .54) as relevant additional predictors. Trust emerged as the most influential predictor overall. The results of moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs compared to devices that combined diagnostic and treatment recommendations. Finally, the relationships between facilitating conditions and use intention was mediated through performance and effort expectancy. CONCLUSIONS The meta-analysis contributes to the understanding of the predictors of the intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and to explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for healthcare practitioners to ease the adoption of AI-CDSSs into their practice. CLINICALTRIAL https://osf.io/b4j3t