Scalable Architecture for Telemonitoring Chronic Diseases in Order to Support the CDSSs in a Common Platform.

Introduction: In recent years, a variety of clinical decision-support systems (CDSS) have been developed to monitor the health of patients with chronic disease from far away. These systems are effective in overcoming human resource limitation and analyzing information generated by Tele-monitoring systems. These systems, however, are limited to monitoring a particular disease, which allows them to be used only in one specific disease. In reuses of these systems to monitor other diseases, we need to re-establish a new system with a new knowledge base. However, this type of healthcare system faces many challenges, including low scalability for change, so that, if we want to modify a health monitoring system designed for a specific disease to be used for another disease, these changes will be very substantial, meaning that, most components of that system should be changed. The lack of scalability in these systems has led to the creation of multiple health monitoring systems, while many of these systems share a common structure.

Aim: In this paper, to solve the scalability problem, architecture has been presented that allows a set of CDSSs to be placed on a common platform for Tele-monitoring.

Material and methods: In order to provide the proposed architecture in this study, we extracted the related concepts from the literature. The anatomical concepts used in these studies are as follow: users, transmitted data, patient data storage databases, data transfer network, and medical setting and the work is done in this setting. Finally, to design the proposed architecture, UML has been used.

Results: The innovation of this research is to provide a scalable and flexible architecture, which as a platform, is able to monitor multiple diseases with a common infrastructure. In this architecture, all components are commonly used simultaneously without the interference of several CDSSs.

Conclusion: Utilizing the proposed model in this paper, while reducing the setup costs and speeding up the launch of various remote monitoring systems, many rework in the implementation of these systems is also reduced.