Edge computing in healthcare: Real-time patient monitoring systems

The proliferation of Internet of Things (IoT) devices in healthcare settings has generated unprecedented volumes of patient data that require efficient processing mechanisms. Edge computing has emerged as a paradigm that allows data processing closer to the source, reducing latency and enabling real-time analytics critical for patient monitoring. This research explores the implementation of edge computing architectures for real-time patient monitoring systems, evaluating their performance across multiple healthcare scenarios. Through experimental deployments in both simulated and real clinical environments, we demonstrate that edge-based monitoring systems reduce data transmission latency by 68% compared to cloud-centric approaches while maintaining 99.7% accuracy in critical parameter monitoring. Our findings indicate that strategic placement of computing resources at the network edge significantly enhances the responsiveness of patient monitoring systems, particularly in time-sensitive medical scenarios. The proposed framework incorporates multi-level data processing with automated triage capabilities, addressing key challenges in contemporary healthcare monitoring including privacy preservation, resource optimization, and reliable operation during network degradation.