Systematic literature review: Utilization of the Internet of Things for real-time patient health monitoring system optimization based on edge computing

The rapid development of the Internet of Things (IoT) has accelerated the digital transformation of healthcare services, particularly in real-time patient health monitoring. However, conventional cloud-based monitoring systems often face challenges related to high latency, bandwidth consumption, and data security. Therefore, this study aims to systematically synthesize the utilization of IoT integrated with edge computing architectures for optimizing real-time patient health monitoring systems. This research employed a Systematic Literature Review (SLR) approach following the PRISMA 2020 guidelines by analyzing publications from IEEE Xplore, PubMed, Scopus, Web of Science, and Google Scholar between 2021 and 2026. From 309 identified articles, 15 studies met the inclusion criteria and were selected for detailed analysis. The findings indicate that hybrid fog-edge architectures can reduce latency by up to 70%, decrease bandwidth usage by 60%, improve energy efficiency by 30%, and achieve clinical detection accuracy ranging from 91% to 99% compared with conventional cloud-based approaches. These results demonstrate that edge computing significantly enhances the responsiveness, reliability, and efficiency of IoT-based patient monitoring systems. Practically, the findings provide valuable guidance for researchers, healthcare institutions, and system developers in designing secure, scalable, and low-latency patient monitoring solutions that support the ongoing digital transformation of healthcare services.