CLINICIANS’ adoption of intelligence-enabled diagnostic clinical decision support systems (CDSS) hinges on performance expectations and perceived risk, with task-technology fit and ease of use acting as critical mediators, according to a cross-sectional survey of 247 clinicians in three Shanghai hospitals.
CDSS, designed to integrate medical knowledge and patient data for enhanced clinical decision-making, faces underutilisation despite its potential. To identify barriers and facilitators, researchers developed a model combining task-technology fit and technology acceptance frameworks. Data were collected via questionnaires assessing demographic factors, multi-item scales, and open-ended feedback, with structural equation modelling applied to analyse relationships between variables. The study spanned four months (December 2023–March 2024) across inpatient and outpatient departments of tertiary hospitals. Results revealed that performance expectations (β=0.228, P<.001) and perceived risk (β=–0.579, P<.001) directly influenced adoption intent, explaining 65.8% of variance. Task-technology fit, driven by task characteristics (β=0.168, P<.001) and technology features (β=0.749, P<.001), reduced perceived risk (β=–0.281, P<.001) and bolstered performance expectations (β=0.508, P<.001). Ease of use indirectly lowered risk perception (β=–0.377, P<.001) but had no direct impact on adoption (β=0.108, P=0.07). Qualitative feedback highlighted concerns over system security, lack of personalised interactions, and poor workflow integration.
To improve CDSS uptake, clinical practice must prioritise transparency in algorithmic processes to mitigate perceived risks, alongside customising interfaces to align with role-specific workflows. Future implementations should focus on seamless integration with existing electronic health records and fostering trust through user involvement in system design. Longitudinal studies are needed to validate these findings and refine predictive models for diverse care settings.
Reference
Zheng R et al. Investigating clinicians’ intentions and influencing factors for using an intelligence-enabled diagnostic clinical decision support system in health care systems: cross-sectional survey. J Med Internet Res 2025;27:e62732.
