Using Complex Event Processing to support data fusion for ambulance coordination

In Emergency Medical Assistance an efficient coordination of the ambulance fleet is crucial for enhancing patient treatment by reducing response times. Basic requirement for ambulance coordination is comprehensive information about the current state of the entire ambulance fleet. State information is usually transmitted by the ambulance crew members. However, manually operated state information is often inaccurate and not always up-to-date due to the stressful work of those professionals. In this paper, we propose a novel event-driven architecture for ambulance coordination in Emergency Medical Assistance. The approach integrates two technologies: (a) Complex Event Processing for intelligent real-time data fusion of sensor data in conjunction with (b) a finite state machine that represents the operational state of an ambulance car. Complex Event Processing gathers and analyses streams of sensor data emitted by the ambulance in order to automatically trigger state transitions of the formally specified state machine model of the vehicle. A major advantage of the approach is its ability to incorporate situation-awareness and reliability in ambulance coordination and, as a consequence, to improve the effectiveness of Emergency Medical Assistance.