人工智能在重症护理中的应用：综述

IF 2.6 3区医学 Q2 CRITICAL CARE MEDICINE

Australian Critical Care Pub Date : 2025-04-04 DOI:10.1016/j.aucc.2025.101225

Yujin Park MSN, RN , Sun Ju Chang PhD, RN , Eunhye Kim PhD, RN

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引用次数: 0

摘要

人工智能（AI）通过改进决策和效率来提高护理质量的潜力，推动了人工智能（AI）与医疗保健的整合正在迅速推进。在重症护理中，患者数据的复杂性和紧迫性至关重要，人工智能技术提供了显著的优势，例如增强患者监测和临床决策支持。目的/目的本综述的目的是综合人工智能在重症护理中的应用及其对患者预后和护理实践的影响的现有文献。方法遵循Arksey和O'Malley的框架，我们对包括PubMed、CINAHL和Embase在内的7个电子数据库进行了系统搜索。如果研究涉及人工智能在重症护理中的应用，或报告人工智能对重症护理环境中患者预后和临床决策的影响，则纳入研究。根据系统评价的首选报告项目和范围评价的元分析扩展清单对文献进行综合。结果纳入了35项涉及该主题的研究。该综述确定了人工智能应用的六个不同领域：监测、护理干预、临床决策支持系统、文档、资源分配和预测分析。预测分析成为最普遍的应用，特别是在预测并发症，如压力损伤和败血症发作。值得注意的是，与临床决策支持系统中更广泛的应用相比，狭隘的人工智能应用表现出了更好的性能，特别是在新生儿疼痛分类等特定任务中。人工智能驱动的文档系统显示出减轻行政负担和提高准确性的希望，而资源分配工具增强了重症监护病房的人员配置优化和工作流程管理。结论：我们的研究结果表明，人工智能在加强重症护理实践方面具有巨大潜力，同时也突出了实施挑战。未来的研究应侧重于制定标准化的实施策略和明确的指导方针，将人工智能整合到护理工作流程中，同时保持技术进步和人类专业知识之间的平衡。RegistrationOpen Science Framework registres http://osf.io/t2y43/。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Artificial intelligence in critical care nursing: A scoping review

Background

The integration of artificial intelligence (AI) into health care has been rapidly advancing, driven by its potential to enhance nursing care quality through improved decision-making and efficiency. Within critical care nursing, where the complexity and urgency of patient data are paramount, AI technologies offer significant advantages, such as enhanced patient monitoring and support in clinical decision-making.

Aim/objective

The aim of this scoping review was to synthesise existing literature on AI applications in critical care nursing and their impact on patient outcomes and nursing practice.

Methods

Following Arksey and O'Malley's framework, we conducted a systematic search across seven electronic databases including PubMed, CINAHL, and Embase. Studies were included if they involved AI applications in critical care nursing or reported on AI's impact on patient outcomes and clinical decision-making in critical care settings. A synthesis of the literature was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist.

Results

Thirty-five studies that addressed this topic were included. The review identified six distinct domains of AI applications: monitoring, nursing intervention, clinical decision support systems, documentation, resource allocation, and predictive analytics. Predictive analytics emerged as the most prevalent application, particularly in forecasting complications such as pressure injuries and sepsis onset. Notably, narrowly focussed AI applications demonstrated superior performance compared to broader applications in clinical decision support systems, particularly in specific tasks like neonatal pain classification. AI-driven documentation systems showed promise in reducing administrative burden and improving accuracy, while resource allocation tools enhanced staffing optimisation and workflow management in intensive care units.

Conclusions

Our findings demonstrate AI's significant potential to enhance critical care nursing practice while highlighting implementation challenges. Future research should focus on developing standardised implementation strategies and clear guidelines for AI integration in nursing workflow while maintaining the balance between technological advancement and human expertise.

Registration

Open Science Framework Registries http://osf.io/t2y43/.

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来源期刊

Australian Critical Care NURSING-NURSING

CiteScore

4.90

自引率

9.10%

发文量

148

审稿时长

>12 weeks

期刊介绍： Australian Critical Care is the official journal of the Australian College of Critical Care Nurses (ACCCN). It is a bi-monthly peer-reviewed journal, providing clinically relevant research, reviews and articles of interest to the critical care community. Australian Critical Care publishes peer-reviewed scholarly papers that report research findings, research-based reviews, discussion papers and commentaries which are of interest to an international readership of critical care practitioners, educators, administrators and researchers. Interprofessional articles are welcomed.