Time is precious for first responders who react to emergency situations such as fires, ambulance calls, motor vehicle accidents, and natural disasters. As police, firefighters, and other emergency workers know all too well, minutes can mean the difference between life and death.
For instance, for every minute a person goes without emergency medical help after suffering a cardiac arrest, that person’s chance of survival goes down 7% to 10%, according to the American Heart Association.
This is one of the primary reasons that first responders are increasingly using big data from a variety of inputs – roadside sensors, security cameras, and GPS systems – to enable them to be more informed and respond more efficiently to accidents and other emergencies.
One of the biggest challenges faced by first responders is the time that elapses between receiving an emergency notification and arriving on the scene.
One of the ways that firefighters, emergency medical personnel, and other responders take advantage of big data analytics to cut response time is through the use of GPS data and technologies.
The use of GPS technologies in the U.K. has reduced travel times for ambulances responding to cardiac incidents by 18%, according to a research report commissioned by Google. And the use of GPS navigation technologies has helped save 152 heart attack victims in the U.K. each year.
Big data analytics can also provide first responders with additional insights that can benefit the people they support.
Big data and analytics were used extensively in response to Superstorm Sandy in 2012 when responders analyzed hashtags, words, and pictures on Twitter and Instagram to help determine where supplies such as food, fuel, and water were needed most, according to an article in Forbes.
While victims of natural disasters don’t always have access to wireless networks and social media channels, these systems can collect a lot of information that can be used by emergency workers and decision makers for relief agencies to identify the top needs of large groups of people.
For instance, firefighters and emergency medical technicians who respond to a fire could receive alerts en route to the scene about hazardous chemicals that may be involved based on information picked up by sensors that’s analyzed and dispersed to emergency teams.
Such information could then be used by firefighters to determine the type of retardant that should be applied to squelch the fire and by medical personnel to identify the types of supplies and treatment needed by the injured.
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