The Impact of Medical Explainable Artificial Intelligence on Nurses’ Innovation Behaviour: A Structural Equation Modelling Approach

IF 3.7 2区 医学 Q2 MANAGEMENT Journal of Nursing Management Pub Date : 2024-09-26 DOI:10.1155/2024/8885760
Xianmiao Li, Qilin Zong, Mengting Cheng
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Abstract

Aim: This study aims to investigate the influence of medical explainable artificial intelligence (XAI) on the innovation behaviour of nurses, as well as explore the dual-pathway mediating effect of AI self-efficacy and AI anxiety and organizational ethical climate as the moderating effect.

Background: To address the practical application of medical AI technology, alleviate the scarcity of medical resources and fulfil the medical and health demands of the public, it is crucial to improve the innovation behaviour of nurses through the use of medical XAI.

Methods: A cross-sectional survey was conducted involving 368 Chinese nurses working at tertiary and secondary hospitals in Anhui Province, Jiangsu Province, Zhejiang Province and Shanghai.

Results: Implementing medical XAI significantly enhanced the innovation behaviour of nurses. Anxiety and self-efficacy regarding AI mediated the connection between medical XAI and the innovation behaviour of nurses. Furthermore, the organizational ethical climate positively moderated the relationship between medical XAI and AI self-efficacy.

Conclusion: Medical XAI helps to enhance nurses’ AI self-efficacy and reduce AI anxiety, thereby enhancing nurses’ innovation behaviour. An organizational ethical climate enhances the positive relationship between medical XAI and AI self-efficacy.

Implications for Nursing Management: Organizations and technology developers must augment the study about XAI and the system design of human-centred AI technology. The organizations aim to enhance the education and training of nurses in AI, specifically focussing on boosting nurses’ self-efficacy in utilizing AI technology. Moreover, they want to alleviate nurses’ fear of new technological advancements. Hospital administrators and leaders develop strategies to address the ethical atmosphere inside their organization.

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医疗可解释人工智能对护士创新行为的影响:结构方程建模方法
目的:本研究旨在探讨医疗可解释人工智能(XAI)对护士创新行为的影响,并探索人工智能自我效能感和人工智能焦虑的双路径中介效应以及组织伦理氛围的调节效应。 研究背景为解决医疗人工智能技术的实际应用问题,缓解医疗资源的稀缺性,满足公众的医疗健康需求,通过使用医疗 XAI 改善护士的创新行为至关重要。 研究方法对在安徽省、江苏省、浙江省和上海市的三级和二级医院工作的 368 名中国护士进行横断面调查。 结果实施医疗 XAI 能明显改善护士的创新行为。对人工智能的焦虑和自我效能感在医疗 XAI 与护士创新行为之间起中介作用。此外,组织伦理氛围正向调节了医疗 XAI 与人工智能自我效能之间的关系。 结论医疗 XAI 有助于增强护士的人工智能自我效能感,降低人工智能焦虑,从而增强护士的创新行为。组织伦理氛围可增强医疗 XAI 与人工智能自我效能感之间的正相关关系。 对护理管理的启示:组织和技术开发人员必须加强对 XAI 的研究和以人为本的人工智能技术的系统设计。组织机构旨在加强对护士在人工智能方面的教育和培训,特别是重点提高护士在使用人工智能技术方面的自我效能感。此外,他们还希望减轻护士对新技术进步的恐惧。医院管理者和领导者制定战略,解决组织内部的道德氛围问题。
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来源期刊
CiteScore
9.40
自引率
14.50%
发文量
377
审稿时长
4-8 weeks
期刊介绍: The Journal of Nursing Management is an international forum which informs and advances the discipline of nursing management and leadership. The Journal encourages scholarly debate and critical analysis resulting in a rich source of evidence which underpins and illuminates the practice of management, innovation and leadership in nursing and health care. It publishes current issues and developments in practice in the form of research papers, in-depth commentaries and analyses. The complex and rapidly changing nature of global health care is constantly generating new challenges and questions. The Journal of Nursing Management welcomes papers from researchers, academics, practitioners, managers, and policy makers from a range of countries and backgrounds which examine these issues and contribute to the body of knowledge in international nursing management and leadership worldwide. The Journal of Nursing Management aims to: -Inform practitioners and researchers in nursing management and leadership -Explore and debate current issues in nursing management and leadership -Assess the evidence for current practice -Develop best practice in nursing management and leadership -Examine the impact of policy developments -Address issues in governance, quality and safety
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