Requirements Evaluation Framework for a Phase‐Driven AIoT Architecture for Remote Patient Monitoring

Abstract

Chronic diseases are long‐lasting diseases that last for three months or longer. Remote Patient Monitoring Systems (RPMS) contain smart and advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) for monitoring patients remotely to enhance patient experience and comfort while reducing hospital visits. Current RPMS architecture often lacks the proper integration of AI and IoT, concentrating on data collection and data transmission without focusing on real‐time processing, predictive analytics and scalability. This research aims to propose an AIoT phase‐driven architecture for RPMS, including evaluating the requirements for managing and monitoring chronic diseases remotely, effectively offering a structured, scalable and intelligent architecture that incorporates IoT for the data collection tasks and AI for real‐time analytics and decision‐making. AIoT phase‐driven architecture is a unique and well‐structured architecture for RPMS, especially by integrating AI and IoT technologies, allowing real‐time monitoring and personalised treatment plans with accurate and precise disease classification. To evaluate and validate the proposed AloT phase‐driven architecture for RPMS, a survey has been conducted with domain experts, which confirms the proposed architecture and the evaluation of the requirements for scalability, practicality and potential to improve chronic disease management with strong support for its phase‐driven approach based on AIoT. The proposed AIoT phase‐driven architecture provides a robust and scalable solution for chronic disease monitoring, enhancing overall patient outcomes and reducing healthcare burdens.