BIG DATA APPLICATIONS IN REMOTE PATIENT MONITORING AND TELEMEDICINE SERVICES: A REVIEW OF TECHNIQUES AND TOOLS

Abstract

This systematic review explores the application of big data in remote patient monitoring (RPM) and telemedicine services, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 52 articles were selected from an initial pool of 85, focusing on the role of enabling technologies such as cloud computing, artificial intelligence (AI), machine learning, and the Internet of Things (IoT). The findings indicate that big data significantly enhances healthcare outcomes by enabling predictive analytics, improving personalized care, and reducing operational costs. Cloud-based platforms facilitate the integration and real-time analysis of large datasets, while AI-driven models improve early detection and intervention for chronic diseases. However, challenges such as data privacy and security concerns, interoperability between healthcare systems, and scalability limitations persist. Additionally, gaps remain in the application of big data for mental health monitoring and underserved populations. This review underscores the need for future research to address these challenges and expand the benefits of big data to diverse healthcare settings.