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ORIGINAL RESEARCH article
Front. Med.
Sec. Regulatory Science
Volume 12 - 2025 |
doi: 10.3389/fmed.2025.1475577
This article is part of the Research Topic Novel Therapies and Emerging Technology in Haemostasis and Thrombosis: Volume II View all 3 articles
Acceptance of Artificial Intelligence Clinical Assistant Decision Support System to Prevent and Control Venous Thromboembolism among Healthcare Workers: An Extend Unified Theory of Acceptance and Use of Technology Model
Provisionally accepted- 1 School of Management, Shanxi Medical University, Jinzhong, China
- 2 Fifth Clinical Medical College, Shanxi Medical University, Taiyuan,, Shanxi Province, China
- 3 Medical Service Division, Shanxi Provincial People's Hospital, Taiyuan, China
Background: Venous thromboembolism (VTE) is an important global health problem and the third most prevalent cardiovascular disorder. It has been proven that computerized tools were helpful in the prevention and control of VTE. However, studies that focused on the acceptance of computerized tools for VTE prevention among healthcare workers were limited.Objective: This study aims to explore what factors are influencing healthcare workers' acceptance of the Artificial Intelligence Clinical Assistant Decision Support System (AI-CDSS) for VTE prevention based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT).We conducted a cross-sectional survey among healthcare workers in three grade-A tertiary hospitals in Shanxi, China. Statistically, the hypothesized model was evaluated by AMOS structural equation modeling.Results: 510 (72.86%) valid surveys were collected in total. The results showed that performance expectancy (β = 0.45, P ＜.001), effort expectancy (β = 0.21, P ＜.001), and top management support (β = 0.30, P ＜.001) positively influenced healthcare workers' intention. Top management support was an antecedent of performance expectancy (β=0.47, P ＜.001), social influence (β=0.57, P ＜.001), effort expectancy (β=0.61, P ＜.001), and information quality (β=0.59, P ＜.001). In addition, Social influence positively influenced performance expectancy(β= 0.52, P ＜.001), and information quality positively influenced system quality (β= 0.59, P ＜.001). Social influence did not influence nurses' behavioral intention (β= 0.05, P =0.376), but negatively influenced clinicians' behavioral intention in the model (β= -0.19, P ＜.001). System quality positively influenced nurses' behavioral intention; ( β= 0.16, P ＜.001), and information quality positively influenced clinicians' behavioral intention (β= 0.15, P =0.025).Conclusions: With this model explaining 76.3% variance of the behavioral intention variable, this study could be useful as a reference for hospital administrators to evaluate future developments and facilitate the implementation of AI-CDSS for VTE prevention.
Keywords: Clinical decision support system, Healthcare worker, Behavioral Intention, Venous Thromboembolism, the unified theory of acceptance and use of technology
Received: 18 Oct 2024; Accepted: 23 Jan 2025.
Copyright: © 2025 Wang, Zhou, Tan, Yu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yun Zhou, Fifth Clinical Medical College, Shanxi Medical University, Taiyuan,, 030001, Shanxi Province, China
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