Modern Intelligent Health Systems: Queueing, Simulation

The Internet of Things and machine learning promise a new era in healthcare. The advent of transformative technologies such as implantable and wearable medical devices (IWMDs) has made it possible to collect and analyze physiological signals from any person at any time. Machine learning allows us to identify patterns in these signals and make health predictions in both every day and clinical situations. This expands healthcare coverage from common clinical contexts to widespread everyday scenarios, from passive data collection to active decision making. Despite the existence of an extensive literature on IWMD-based and clinical health systems, the fundamental problems associated with the design and implementation of smart health systems have not been adequately addressed. The main objectives of this article are to define a standard framework for smart health, designed for both every day and clinical settings, to explore modern smart health systems and their constituent components. Queuing theory provides robust methods to evaluate alternative designs for modeling organization smart healthcare.