Artificial intelligence-assisted nursing care: a concept analysis using Walker and Avant approach.

IF 3.9 2区 医学 Q1 NURSING
Reza Nematollahi Maleki, Shahla Shahbazi, Mina Hoseinzadeh, Mansour Ghafourifard, Hamed Gholizad Gougjehyaran, Amir Faravan
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Abstract

Background: Artificial Intelligence (AI) is increasingly integrated into healthcare, offering transformative potential for nursing practice by enhancing efficiency, accuracy, and patient outcomes. Despite growing interest, the concept of AI-assisted nursing care lacks clear consensus, hindering its clinical operationalization. This study aims to clarify this concept through a concept analysis to inform future research and practice.

Methods: The Walker and Avant concept analysis method was utilized to examine 'AI-assisted nursing care.' A literature review was conducted across databases including PubMed, Scopus, ScienceDirect, and Embase, with no temporal limits, yielding 20 relevant records for analysis. The process identified the concept's uses, attributes, antecedents, consequences, and empirical referents.

Results: Five defining attributes of AI-assisted nursing care emerged: data-driven decision support, automation of routine tasks, enhanced predictive capabilities, personalization of care, and continuous learning and adaptability. Antecedents included availability of advanced technology, integration into healthcare systems, nursing competence and acceptance, patient data availability, and ethical and regulatory frameworks. Consequences encompassed improved patient outcomes, increased nursing efficiency, enhanced nurses' satisfaction, potential cost savings, and ethical and social challenges. Model, borderline, and contrary cases further illustrated the concept's application.

Conclusion: AI-assisted nursing care holds significant promise for revolutionizing clinical practice by improving care quality and nursing workflows. However, its implementation demands addressing technological, ethical, and systemic challenges. Future research should prioritize empirical validation of these findings and promote equitable access to AI technologies across diverse healthcare settings to fully realize its potential.

Abstract Image

人工智能辅助护理:使用Walker和Avant方法的概念分析。
背景:人工智能(AI)越来越多地融入医疗保健领域,通过提高效率、准确性和患者预后,为护理实践提供了变革性的潜力。尽管人们对人工智能辅助护理的兴趣日益浓厚,但人工智能辅助护理的概念缺乏明确的共识,阻碍了其临床操作。本研究旨在通过概念分析来厘清这一概念,为未来的研究和实践提供信息。方法:采用Walker和Avant概念分析法对“人工智能辅助护理”进行检验。在PubMed、Scopus、ScienceDirect和Embase等数据库中进行了文献综述,没有时间限制,产生了20条相关记录用于分析。这个过程确定了概念的用途、属性、先行词、结果和经验指涉物。结果:人工智能辅助护理出现了五个定义属性:数据驱动的决策支持、日常任务的自动化、增强的预测能力、个性化护理、持续学习和适应性。前因包括先进技术的可用性、与医疗保健系统的整合、护理能力和接受度、患者数据的可用性以及道德和监管框架。结果包括改善患者预后,提高护理效率,提高护士满意度,潜在的成本节约以及道德和社会挑战。模型、边界和相反的案例进一步说明了这一概念的应用。结论:人工智能辅助护理通过改善护理质量和护理工作流程,有望彻底改变临床实践。然而,它的实施需要解决技术、伦理和系统方面的挑战。未来的研究应优先考虑对这些发现进行实证验证,并促进在不同医疗环境中公平获取人工智能技术,以充分发挥其潜力。
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来源期刊
BMC Nursing
BMC Nursing Nursing-General Nursing
CiteScore
3.90
自引率
6.20%
发文量
317
审稿时长
30 weeks
期刊介绍: BMC Nursing is an open access, peer-reviewed journal that considers articles on all aspects of nursing research, training, education and practice.
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