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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Reza Nematollahi Maleki,
  • Shahla Shahbazi,
  • Mina Hosseinzadeh,
  • Mansour Ghafourifard,
  • Hamed Gholizad Gougjehyaran,
  • Amir Faravan

摘要

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.