Investigating Clinicians' Intentions and Influencing Factors for Using Intelligence-enabled Clinical Decision Support Systems in Healthcare Systems：Cross-sectional Survey (Preprint)

Abstract

BACKGROUND Intelligence-enabled clinical decision support systems (CDSS) are sophisticated software systems designed to assist healthcare providers in making clinical decisions by leveraging various forms of intelligence. Research studies have shown that CDSS utilization rates have not met expectations. Clinicians' intentions and their attitudes determine the use and promotion of CDSS in clinical practice. OBJECTIVE The aim of this study was to enhance the successful utilization of CDSS by analyzing the pivotal factors that influence clinicians' intention to adopt it and by putting forward targeted management recommendations. METHODS This study proposed a research model grounded in the Task-Technology Fit (TTF) model and the Technology Acceptance Model (TAM), which was then tested through a cross-sectional survey. The measurement instrument comprised demographic characteristics, multi-item scales, and an open-ended query regarding areas where clinicians perceived the system required improvement. We leveraged structural equation modeling to assess the direct and indirect effects of “Task-Technology Fit” and “Perceived Ease of Use” on clinicians’ intention to use the CDSS when mediated by“Performance Expectation” and “Perceived Risk”. We collated and analyzed the responses to the open-ended question. RESULTS We collected a total of 247 questionnaires. The model explained 65.8% of the variance in use intention. Performance expectations (β=0.228; P<0.001) and perceived risk (β=-0.579; P<0.001) were both significant predictors of use intention. Task-technology fit(β=-0.281;P<0.001) and perceived ease of use(β=-0.377;P<0.001) negatively affected perceived risk. Perceived risk(β=-0.308;P<0.001) negatively affected performance expectations. Task-technology fit positively affected perceived ease of use(β=0.692;P<0.001) and performance expectations(β=0.508;P<0.001). Task characteristics(β=0.168;P<0.001) and technology characteristics(β=0.749;P<0.001) positively affected Task-technology fit. Contrary to expectations, perceived ease of use(β=0.108;P=0.073) did not have a significant impact on use intention. From the open-ended question, three main themes emerged regarding clinician’s perceived deficiencies in CDSS: system security risks, personalized interaction, seamless integration. CONCLUSIONS Perceived risk and performance expectations were direct determinants of clinicians' adoption of CDSS, significantly influenced by task-technology fit and perceived ease of use. In the future, increasing transparency within CDSS, fostering trust between clinicians and technology should be prioritized. Furthermore, focusing on personalized interactions and ensuring seamless integration into clinical workflows are crucial steps moving forward.