Architectural Patterns for Building Scalable Cloud Platforms for Continuous Remote Cardiac Monitoring of Patients

The article examines a set of architectural patterns that enable the construction of scalable cloud platforms for continuous remote cardiac monitoring of patients amid growing demand for timely detection of cardiovascular disorders. The relevance of the study is determined by the limitations of traditional outpatient monitoring methods, which often record clinically significant arrhythmia episodes with substantial delay, thereby reducing the effectiveness of early intervention and increasing the risk of adverse outcomes. The aim of the work is to develop an architectural model of a cloud platform capable of providing reliable, secure, and near-real-time processing of streaming ECG data within the Internet of Medical Things environment. The scientific novelty of the article lies in substantiating the concept of seamless monitoring by synthesizing edge-based signal filtering, Bluetooth gateway data transmission, and a microservice-based, event-driven cloud infrastructure. As key conclusions, it is shown that the use of a Bluetooth gateway makes it possible to eliminate the critical gap between ECG recording and interpretation, that a microservice architecture in combination with Kafka and Raft ensures horizontal scalability and fault tolerance, and that a hybrid edge-cloud processing model improves analytical accuracy and reduces the latency of processing critical events. The article will be useful for researchers, architects of medical IT systems, engineers of IoMT platforms, and specialists in digital healthcare.