评估临床决策支持系统（AI-抗谵妄）在提高重症监护病房护士对谵妄指南依从性方面的有效性。
Evaluating the effectiveness of a clinical decision support system (AI-Antidelirium) to improve Nurses' adherence to delirium guidelines in the intensive care unit.
作者信息
Zhang Shan, Ding Shu, Cui Wei, Li Xiangyu, Wei Jun, Wu Ying
机构信息
School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District Beijing, China.
Beijing Chao-Yang Hospital, Capital Medical University, Beijing, CN 100020, China.
出版信息
Intensive Crit Care Nurs. 2025 Apr;87:103933. doi: 10.1016/j.iccn.2024.103933. Epub 2025 Jan 8.
OBJECTIVES
To evaluate the impact of Artificial Intelligence Assisted Prevention and Management for Delirium (AI-AntiDelirium) on improving adherence to delirium guidelines among nurses in the intensive care unit (ICU).
RESEARCH METHODOLOGY/DESIGN: Between November 2022 and June 2023, A cluster randomized controlled trial was undertaken.
SETTING
A total of 38 nurses were enrolled in the interventional arm, whereas 42 nurses were recruited for the control arm in six ICUs across two hospitals in Beijing, comparing nurses' adherence and cognitive load in units that use AI-AntiDelirium or the control group.
MAIN OUTCOME MEASURES
The AI-AntiDelirium tailored delirium preventive or treated interventions to address patients' specific risk factors. The adherence rate of delirium interventions was the primary endpoint. The other endpoints were adherence to risk factors assessment, ICU delirium assessment, and nurses' cognitive load. The repeated measures analysis of variance was utilized to explore the influence of time, group, and time × group interaction on the repeated measurement variable (e.g., adherence, cognitive load).
RESULTS
A cumulative total of 1040 nurse days were analyzed for this study. The adherence to delirium intervention of nurses in AI-AntiDelirium groups was higher than control units (75 % vs. 58 %, P < 0.01). When compared to control groups, AI-AntiDelirium was found to be significantly effective in both decreasing extraneous cognitive load (P < 0.01) and improving germane cognitive load (P < 0.01).
CONCLUSIONS
This study supports the effectiveness of AI-AntiDelirium in enhancing nurses' adherence to evidence-based, individualized delirium intervention and also reducing extraneous cognitive load.
IMPLICATIONS FOR CLINICAL PRACTICE
A nurse-led systemshould be applied by nursing administrators to improve compliance with nursing interventions among ICU nurses.
目的
评估人工智能辅助谵妄预防与管理（AI-抗谵妄）对提高重症监护病房（ICU）护士遵循谵妄指南情况的影响。
研究方法/设计：在2022年11月至2023年6月期间，进行了一项整群随机对照试验。
研究背景
在北京两家医院的六个ICU中，共有38名护士被纳入干预组，42名护士被招募到对照组，比较使用AI-抗谵妄的科室与对照组护士的依从性和认知负荷。
主要观察指标
AI-抗谵妄针对患者的特定风险因素制定谵妄预防或治疗干预措施。谵妄干预的依从率是主要终点。其他终点包括对风险因素评估的依从性、ICU谵妄评估以及护士的认知负荷。采用重复测量方差分析来探讨时间、组间以及时间×组间交互作用对重复测量变量（如依从性、认知负荷）的影响。
结果
本研究共分析了1040个护士工作日。AI-抗谵妄组护士对谵妄干预的依从性高于对照组（75%对58%，P<0.01）。与对照组相比，发现AI-抗谵妄在降低无关认知负荷（P<0.01）和提高相关认知负荷方面均具有显著效果（P<0.01）。
结论
本研究支持AI-抗谵妄在提高护士对循证、个体化谵妄干预的依从性以及降低无关认知负荷方面的有效性。
对临床实践的启示
护理管理人员应采用以护士为主导的系统，以提高ICU护士对护理干预的依从性。
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