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Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan
Published by Universitas Harapan Bangsa Purwokerto
ISSN : 19791034     EISSN : 26561034     DOI : -
Core Subject : Health,
Health Professions Medicine & Pharmacology Nursing Public Health
Viva Medika Is a journal that publishes articles or research results relating to health, nursing and midwifery issues. Viva Medika is published by Harapan Bangsa University twice a year (September and February). The mission of the Journal of Viva Medika is to disseminate and discuss scientific writings on midwifery, nursing, and various issues within the scope of health problems. This journal is intended as a medium of communication for lecturers and people who have attention to health, obstetrics, nursing.
Arjuna Subject : -
Articles 601 Documents
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AI Agents for Nursing Task Augmentation: A Focused Literature Overview of Clinical, Operational, Educational, and Governance Implications Sony Kartika Wibisono; Purwono Purwono; Muhammad Ahmad Baballe; Imam Ahmad Ashari; Annastasya Nabila Elsa Wulandari
Viva Medika Vol 19 No 2 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v19i2.2250

Abstract

The rapid development of artificial intelligence has accelerated the emergence of AI agents, defined as autonomous or semi-autonomous systems that integrate perception, contextual reasoning, decision-making, interaction, and action within defined workflows. Although AI in nursing has been widely reviewed, existing syntheses often combine predictive models, generative tools, decision-support systems, and agent-based architectures, leaving the specific contributions and implementation maturity of AI agents insufficiently differentiated. This focused literature overview examined peer-reviewed publications published between 2021 and 2025 using targeted database searches and a structured narrative synthesis. The review classified the evidence according to agent architecture, automated nursing tasks, implementation maturity, and reported clinical and operational implications. Three overlapping architectural categories were identified: LLM-driven agents, cognitive agents, and multi-agent systems. Applications were concentrated in clinical documentation and handover, predictive monitoring, medication safety, triage, and clinical decision support. The evidence suggests potential improvements in timeliness, documentation consistency, risk detection, workflow integration, and the reduction of repetitive administrative work. The maturity of the evidence varied considerably. Monitoring and sensor-enabled safety systems showed closer links to rsmitheal-world practice, whereas LLM-driven documentation and multi-agent triage systems were more frequently supported by conceptual, prototype, or simulation-based evidence. The synthesis therefore supports supervised task augmentation rather than the replacement of professional nursing judgment. Major implementation requirements include system reliability, bias mitigation, transparent accountability, human oversight, workforce competency, organizational readiness, and clinical governance. Nursing education should prepare practitioners to evaluate AI-generated outputs, recognize uncertainty and inappropriate recommendations, and apply appropriate escalation procedures. Future research should prioritize real-world, multisite, and longitudinal evaluations that measure patient safety, workload redistribution, verification burden, and clinical outcomes. This review provides a focused conceptual distinction between conventional AI decision-support tools and agent-based systems while integrating their clinical, operational, educational, and governance implications.

Page 61 of 61 | Total Record : 601

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