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Rabeifar F. Big Data Analytics, Machine Learning, Telemedicine, Nursing Informatics, Patient Safety, Systematic Review. Mod Care J 2026; 23 (3)
URL: http://mcj.bums.ac.ir/article-1-606-en.html
URL: http://mcj.bums.ac.ir/article-1-606-en.html
Department of Computer Engineering, ShQ.C., Islamic Azad University, Shahr-e Qods, Iran. Email: fa.rabeifar@iau.ac.ir
Abstract: (355 Views)
Context: Telemedicine has become a central component of modern healthcare, enabling remote and accessible care delivery through digital innovations. The integration of big data analytics (BDA) and Machine learning (ML) offers significant potential to enhance nursing practice, strengthen patient safety, and improve care quality. However, the existing literature on their applications remains fragmented, underscoring the need for a comprehensive synthesis.
Objectives: This review aims to systematically examine how BDA and ML are utilized in telemedicine to support nursing practice, promote patient safety, and enhance care quality. It further identifies key challenges and outlines future directions for the ethical and sustainable integration of these technologies. Evidence Acquisition: This systematic review was conducted using English and Persian peer-reviewed articles, systematic reviews, and grey literature published between Jan 2015 and April 2025 (PRISMA).
Data Sources: The databases searched included PubMed, CINAHL, Scopus, and IEEE Xplore. Eligible studies addressed the application of BDA or ML in telemedicine concerning nursing workflows, patient safety, or care quality. Out of 623 screened records, 47 studies met inclusion criteria and were thematically analyzed.
Results: The review indicates that BDA and ML are widely applied in predictive analytics for early deterioration detection, personalized care planning, and continuous remote monitoring with automated alerts. These technologies assist nurses by optimizing workflows, reducing administrative tasks, and enhancing evidence-based decision-making. Reported benefits include fewer medication errors, timely interventions, and improved monitoring of high-risk patients. Nevertheless, barriers such as privacy and security issues, algorithmic bias, interoperability challenges, and the need for enhanced digital literacy among nurses persist.
Conclusions: The BDA and ML hold transformative potential to advance telemedicine by empowering nurses with actionable, data-driven insights that improve patient safety and care quality. Achieving sustainable integration will require interdisciplinary collaboration, ethical artificial intelligence (AI) frameworks, robust data governance, and targeted educational initiatives to ensure equitable and effective implementation. Future implementations should prioritize nurse-led AI governance and ongoing digital competency training programs.
Objectives: This review aims to systematically examine how BDA and ML are utilized in telemedicine to support nursing practice, promote patient safety, and enhance care quality. It further identifies key challenges and outlines future directions for the ethical and sustainable integration of these technologies. Evidence Acquisition: This systematic review was conducted using English and Persian peer-reviewed articles, systematic reviews, and grey literature published between Jan 2015 and April 2025 (PRISMA).
Data Sources: The databases searched included PubMed, CINAHL, Scopus, and IEEE Xplore. Eligible studies addressed the application of BDA or ML in telemedicine concerning nursing workflows, patient safety, or care quality. Out of 623 screened records, 47 studies met inclusion criteria and were thematically analyzed.
Results: The review indicates that BDA and ML are widely applied in predictive analytics for early deterioration detection, personalized care planning, and continuous remote monitoring with automated alerts. These technologies assist nurses by optimizing workflows, reducing administrative tasks, and enhancing evidence-based decision-making. Reported benefits include fewer medication errors, timely interventions, and improved monitoring of high-risk patients. Nevertheless, barriers such as privacy and security issues, algorithmic bias, interoperability challenges, and the need for enhanced digital literacy among nurses persist.
Conclusions: The BDA and ML hold transformative potential to advance telemedicine by empowering nurses with actionable, data-driven insights that improve patient safety and care quality. Achieving sustainable integration will require interdisciplinary collaboration, ethical artificial intelligence (AI) frameworks, robust data governance, and targeted educational initiatives to ensure equitable and effective implementation. Future implementations should prioritize nurse-led AI governance and ongoing digital competency training programs.
Article Types: Systematic Review |
Subject:
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Received: 2026/02/10 | Accepted: 2026/02/2 | Published: 2026/07/19
Received: 2026/02/10 | Accepted: 2026/02/2 | Published: 2026/07/19
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