Emergency Departments (ED) supply critical infrastructure to provide medical care in the event of a disaster or disease outbreak, including seasonal and pandemic influenza [1]. Already over-crowded and stretched to near-capacity, influenza activity augments patient volumes and increases ED crowding [2,3]; high ED patient volumes expected during a true influenza pandemic represents a significant threat to the nation's healthcare infrastructure [4]. EDs ability to manage both seasonal and pandemic influenza surges is dependent on coupling early detection with graded rapid response. Although many EDs have devised influenza response measures, the potential utility of coupling early warning systems with various response strategies for managing influenza outbreaks in the ED setting has not been rigorously studied. While practical use of traditional surveillance systems has been limited due to the several week lag associated with reporting, new internet-based surveillance tools, such as GFT, report surveillance data in near-real time, thus allowing rapid integration into healthcare response planning [5].
Objective
Google Flu Trends (GFT) is a novel internet-based influenza surveillance system that uses search engine query data to estimate influenza activity. This study assesses the temporal correlation of city GFT data to both confirmed cases of influenza, as well as standard crowding indices from one inner-city emergency department (ED).