Artificial intelligence in nursing: an integrative review of clinical and operational impacts DOI Creative Commons

Salwa Hassanein,

Rabie Adel El Arab, Amany Abdrbo

et al.

Frontiers in Digital Health, Journal Year: 2025, Volume and Issue: 7

Published: March 7, 2025

Background Advances in digital technologies and artificial intelligence (AI) are reshaping healthcare delivery, with AI increasingly integrated into nursing practice. These innovations promise enhanced diagnostic precision, improved operational workflows, more personalized patient care. However, the direct impact of on clinical outcomes, workflow efficiency, staff well-being requires further elucidation. Methods This integrative review synthesized findings from 18 studies published through November 2024 across diverse settings. Using PRISMA 2020 SPIDER frameworks alongside rigorous quality appraisal tools (MMAT ROBINS-I), examined multifaceted effects integration nursing. Our analysis focused three principal domains: advancements monitoring, efficiency workload management, ethical implications. Results The demonstrates that has yielded substantial benefits. AI-powered monitoring systems, including wearable sensors real-time alert platforms, have enabled nurses to detect subtle physiological changes—such as early fever onset or pain indicators—well before traditional methods, resulting timely interventions reduce complications, shorten hospital stays, lower readmission rates. For example, several reported early-warning algorithms facilitated faster responses, thereby improving safety outcomes. Operationally, AI-based automation routine tasks (e.g., scheduling, administrative documentation, predictive classification) streamlined resource allocation. efficiencies led a measurable reduction nurse burnout job satisfaction, can devote time despite these benefits, challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, potential erosion judgment due overreliance technology. issues underscore need for robust targeted literacy training within curricula. Conclusion holds transformative practice by enhancing both outcomes efficiency. realize benefits fully, it is imperative develop frameworks, incorporate comprehensive education, foster interdisciplinary collaboration. Future longitudinal varied contexts essential validate support sustainable, equitable implementation Policymakers leaders must prioritize investments solutions complement expertise professionals while addressing risks.

Language: Английский

Artificial intelligence in nursing: an integrative review of clinical and operational impacts DOI Creative Commons

Salwa Hassanein,

Rabie Adel El Arab, Amany Abdrbo

et al.

Frontiers in Digital Health, Journal Year: 2025, Volume and Issue: 7

Published: March 7, 2025

Background Advances in digital technologies and artificial intelligence (AI) are reshaping healthcare delivery, with AI increasingly integrated into nursing practice. These innovations promise enhanced diagnostic precision, improved operational workflows, more personalized patient care. However, the direct impact of on clinical outcomes, workflow efficiency, staff well-being requires further elucidation. Methods This integrative review synthesized findings from 18 studies published through November 2024 across diverse settings. Using PRISMA 2020 SPIDER frameworks alongside rigorous quality appraisal tools (MMAT ROBINS-I), examined multifaceted effects integration nursing. Our analysis focused three principal domains: advancements monitoring, efficiency workload management, ethical implications. Results The demonstrates that has yielded substantial benefits. AI-powered monitoring systems, including wearable sensors real-time alert platforms, have enabled nurses to detect subtle physiological changes—such as early fever onset or pain indicators—well before traditional methods, resulting timely interventions reduce complications, shorten hospital stays, lower readmission rates. For example, several reported early-warning algorithms facilitated faster responses, thereby improving safety outcomes. Operationally, AI-based automation routine tasks (e.g., scheduling, administrative documentation, predictive classification) streamlined resource allocation. efficiencies led a measurable reduction nurse burnout job satisfaction, can devote time despite these benefits, challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, potential erosion judgment due overreliance technology. issues underscore need for robust targeted literacy training within curricula. Conclusion holds transformative practice by enhancing both outcomes efficiency. realize benefits fully, it is imperative develop frameworks, incorporate comprehensive education, foster interdisciplinary collaboration. Future longitudinal varied contexts essential validate support sustainable, equitable implementation Policymakers leaders must prioritize investments solutions complement expertise professionals while addressing risks.

Language: Английский

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