Efficient Remote Patient Monitoring Using Multi-Parameter Devices and Cloud with Priority-Based Data Transmission Optimization

Abstract

Increasingly, cloud computing is transforming how data is managed and analyzed financially, providing scalable and secure solutions for SMEs and healthcare applications. Nevertheless, the remote patient monitoring (RPM) systems that have been established thus far encounter problems such as high latency, a poor usage of available bandwidth, as well as vulnerabilities in data security, thus restricting healthcare decision-making in real-time. Traditional encryption and authentication methods do not hold enough value against new-age evolving cyber threats and thus risk patient data privacy, whereas established means of data transmission do not prioritize significant health parameters leading to bring about delayed emergency response. E-RPM revolves proposing a very cost effective and safe Remote Patient Monitoring system on an integrated multi-parameter wearable device, cloud computing, and priority-based optimization of data transmission process for real-time health monitoring. Threats are detected using AI on hybrid encrypted data and then authenticated through multiple factors so that the system provides integrity of data as well as patient privacy and secured communication. The crux of this includes noise reduction and normalization of health data preprocessing, compression of data for high efficiency in storage and transmission, then the last nugget would be Convolutional Neural Networks (CNN) for predictive health analytics. It decreases congestion and proposes a priority-based transmission model which utilize bandwidth for real-time delivery of critical health data and reduced latencies.