Monitoring and Identifying in Real time Critical Patients Events.

Nowadays pervasive health care monitoring environments, as well as business activity monitoring environments, gather information from a variety of data sources. However it includes new challenges because of the use of body and wireless sensors, nontraditional operational and transactional sources. This makes the health data more difficult to monitor. Decision making in this environment is typically complex and unstructured as clinical work is essentially interpretative, multitasking, collaborative, distributed and reactive. Thus, the health care arena requires real time data management in areas such as patient monitoring, detection of adverse events and adaptive responses to operational failures. This research presents a new architecture that enables real time patient data management through the use of intelligent data sources.