Edge-Based Telemedical Architecture Integrating Edge Devices and Lightweight AI for Healthcare in Kenya

Background: The growing burden of multimorbidity, which is the occurrence of two or more chronic conditions simultaneously in an individual is a great burden on the healthcare system of Kenya that is already defined by unequal distribution of resources and access to specialists. The solution to this gap may be found in telemedicine, but due to the dependency on centralized cloud computing, the effectiveness in the context of the resources-constrained environment is critically impaired. This dependence leads to chronic problems such as unreliable internet connection, power connectivity issues, large data latency making real-time intervention difficulty, excessive bandwidth prices, and, ultimately, piecemeal care of the multimorbid patients. Methods: The study employed the use of a combination of various approaches as it started with an extensive analysis of the current state of telemedicine and its limitation in managing multimorbidity both globally and locally. Then, an architectural framework of a conceptual edge-based framework was designed that defined the primary elements of local data processing, offline functionality, and real-time clinical decision support. Results: This study developed a new, edge-computing telemedical architecture that moves vital data processing and storage operations out of the cloud and on to local edge nodes (edge devices) at the point of care. The main aim was to develop a more robust, effective and responsive telehealth system that will be able to work efficiently even in low-connectivity settings. The possible effect of this framework was compared to the key performance indicators showing that there was a substantial theoretical reduction in the latency of data transmission and the bandwidth used. Moreover, the telemedical model is projected to improve continuity and coordination of care through the processing and acting of critical patient data about IoT devices such as blood pressure, glucose monitoring locally even when the internet is unavailable. Conclusion: Integration of edge computing is a feasible and strategic solution to the basic infrastructural constraints of cloud-based telemedicine in Kenya. The suggested model does not only overcome technical obstacles of connectivity and cost, but also presents a foundational framework of scalable, patient-centered and integrated care to the increasing population of multimorbid patients.