Awad Nadia Hassan Ali, Aljohani Wafaa, Yaseen Mai Mohammed, Awad Wafaa Hassan Ali, Abou Elala Randa Ahmed Said Ahmed, Ashour Heba Mohammed Alanwer
Nursing Administration Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt.
Nursing Program, Batterjee Medical College, Jeddah, Saudi Arabia.
Nurs Crit Care. 2025 Sep;30(5):e70157. doi: 10.1111/nicc.70157.
Artificial intelligence (AI)-based Clinical Decision Support Systems (AI-CDSS) are increasingly implemented in intensive care settings to support nurses in complex, time-sensitive decisions, aiming to improve accuracy, efficiency and patient outcomes. However, their use raises concerns about emotional consequences, particularly decision regret, which may arise when clinical judgement or outcomes are unfavourable. Trust in AI may play a key role in shaping nurses' responses to AI-guided decisions.
To examine the relationship between nurses' reliance on AI-CDSS, decision regret and trust in AI, with a focus on the moderating role of trust in the association between AI-CDSS reliance and decision regret.
A cross-sectional correlational design was used. A convenience sample of 250 intensive care unit (ICU) nurses completed validated instruments: the Healthcare Systems Usability Scale (HSUS) for AI-CDSS reliance, the Decision Regret Scale (DRS) and the Trust in AI Scale. Descriptive statistics, Pearson's correlations, multiple linear regression and moderation analysis were conducted.
A total of 250 ICU nurses participated in the study out of 400 approached, yielding a response rate of 62.5%. Nurses reported moderate levels of AI-CDSS reliance (M = 78.6, SD = 12.4), decision regret (M = 38.5, SD = 14.8) and trust in AI (M = 13.9, SD = 3.2). AI-CDSS reliance was negatively correlated with decision regret (r = -0.42, p < 0.01) and positively with trust in AI (r = 0.51, p < 0.01). Regression analysis showed that both AI-CDSS reliance (β = -0.36) and trust in AI (β = -0.24) significantly predicted reduced regret (R = 0.27, p < 0.001). Trust moderated the relationship, strengthening the negative association between reliance and regret.
Greater reliance on AI-CDSS is associated with lower decision regret among ICU nurses, especially when trust in AI is high. Trust enhances emotional acceptance and supports effective AI integration.
Building trust in AI-CDSS among nurses is essential for minimising emotional burden and optimising decision-making in critical care.
基于人工智能（AI）的临床决策支持系统（AI - CDSS）在重症监护环境中越来越多地得到应用，以协助护士做出复杂且时间紧迫的决策，旨在提高准确性、效率和患者预后。然而，其使用引发了对情感后果的担忧，尤其是当临床判断或结果不理想时可能出现的决策后悔情绪。对人工智能的信任可能在塑造护士对人工智能引导决策的反应方面发挥关键作用。
探讨护士对AI - CDSS的依赖、决策后悔与对人工智能的信任之间的关系，重点关注信任在AI - CDSS依赖与决策后悔之间关联中的调节作用。
采用横断面相关设计。对250名重症监护病房（ICU）护士进行便利抽样，他们完成了经过验证的量表：用于评估对AI - CDSS依赖程度的医疗系统可用性量表（HSUS）、决策后悔量表（DRS）和对人工智能的信任量表。进行了描述性统计、Pearson相关性分析、多元线性回归分析和调节分析。
在邀请的400名护士中，共有250名ICU护士参与了研究，回复率为62.5%。护士报告的对AI - CDSS的依赖程度适中（M = 78.6，标准差 = 12.4），决策后悔程度适中（M = 38.5，标准差 = 14.8），对人工智能的信任程度适中（M = 13.9，标准差 = 3.2）。对AI - CDSS的依赖与决策后悔呈负相关（r = -0.42，p < 0.01），与对人工智能的信任呈正相关（r = 0.51，p < 0.01）。回归分析表明，对AI - CDSS的依赖（β = -0.36）和对人工智能的信任（β = -0.24）均能显著预测后悔程度的降低（R = 0.27，p < 0.oooo1）。信任起到了调节作用，加强了依赖与后悔之间的负相关关系。
ICU护士对AI - CDSS的依赖程度越高，决策后悔程度越低，尤其是当对人工智能的信任度较高时。信任增强了情感接受度，并支持有效的人工智能整合。
在护士中建立对AI - CDSS的信任对于减轻重症监护中的情感负担和优化决策至关重要。
