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All Journal Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan
Purwono Purwono
Peneliti Teknologi Teknik Indonesia
Health Professions Medicine & Pharmacology Nursing Public Health

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A Scoping Review of Machine Learning Applications in Nursing Practice: Clinical Decision Support, Risk Prediction, and Workflow Optimization Anton Suhendro; Wahyu Caesarendra; Purwono Purwono
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2222

Abstract

Machine learning (ML) is rapidly transforming nursing practice by enabling advancements in clinical decision support, risk prediction, and workflow optimization. This scoping review synthesizes evidence from empirical studies, reviews, and implementation reports published between 2018 and 2025, identified through Scopus and ScienceDirect. The findings indicate that supervised learning algorithms, deep learning, and natural language processing are widely utilized for risk assessment, early detection of patient deterioration, and enhancement of administrative efficiency. Natural language processing (NLP) also supports automation of nursing documentation and improved data quality. Despite favorable performance metrics, including AUROC values above 0.85 in many applications, most studies are limited by single-institution data, insufficient external validation, and heterogeneous reporting standards. Major barriers include ethical and legal concerns, data quality issues, algorithmic bias, infrastructural limitations, and limited nurse involvement in model development. Enhancing AI literacy and fostering nurse engagement in system design are highlighted as critical for successful clinical integration. Future research priorities include multicenter validation, development of explainable AI, adoption of standardized reporting guidelines, and interdisciplinary collaboration to address ethical, technical, and regulatory challenges. Overall, this scoping review demonstrates that machine learning offers substantial potential to improve patient outcomes and nursing operations, but responsible adoption requires rigorous validation, transparent governance, and active participation of nursing professionals throughout the technology lifecycle
Artificial Intelligence Applications in Community and Home Nursing Care: A Systematic Literature Review Berliana Rahmadhani; Purwono Purwono; Muhammad Ahmad Baballe; Isa Ali Ibrahim
Viva Medika Vol 19 No 1 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v19i1.2235

Abstract

Healthcare systems face increasing demand for community and home nursing care due to population aging, chronic disease prevalence, and hospital resource limitations. Artificial intelligence (AI) has emerged as a supportive technology with potential to enhance nursing practice in decentralized care environments. This systematic literature review synthesizes recent evidence on AI applications in community and home nursing care. The review followed PRISMA 2020 guidelines and analyzed fifteen peer-reviewed studies published between 2022 and 2025. The findings indicate that machine learning–based predictive analytics and decision-support systems are the most frequently implemented technologies. AI applications primarily support risk prediction, remote monitoring, chronic disease management, and workflow optimization. Reported outcomes include improved clinical vigilance, enhanced care coordination, and increased operational efficiency. However, implementation challenges remain, including infrastructure readiness, digital literacy gaps, ethical governance concerns, and data privacy risks. Overall, AI functions as an augmentative tool that strengthens professional nursing judgment rather than replacing it. Sustainable integration in community and home nursing care requires digital competence, regulatory alignment, and human-centered implementation strategies.
Co-Authors Anton Suhendro Berliana Rahmadhani Isa Ali Ibrahim Muhammad Ahmad Baballe Wahyu Caesarendra