Real-Time Capacity Management and Patient Flow Optimization in Hospitals Using AI Methods

Hospital systems are under constant pressure to provide quality care despite limited resources. However, traditional capacity management in hospitals is often not effective enough. One reason for this is the variability and uncertainty in the healthcare field that has to be managed. Another reason is the observation that hospitals are open loop systems, meaning they do not use feedback to determine if their output has achieved the desired goal of input. They do not observe the output of their processes controlled by them and use this information to take action. In hospital systems, there are few efficient planning systems or decision support systems to help administrators take decisions. This is different in other industries, where complex planning systems with the help of Artificial Intelligence, or AI as it is referred to, are often being used. This research chapter analyses the issues and possibilities for hospitals to incorporate AI into their capacity management and become intelligent systems in which operations and processes are regulated by feedback (closed loop system) and, more specifically, discusses the recent research of the authors on this topic, where Artificial Intelligent (Multi-Agent System) methods in combination with real-time coordination were described and implemented in the Aravind Eye Hospital (AEH) in India.