Crash data promises to make big impact on emergency medical sector
HOUSTON — Automatic crash notification (ACN) data generated by in-vehicle telematics systems one day will be used by 911 dispatchers to predict injury severity, according to experts speaking yesterday at the Association of Public Safety Communications Officials conference. The ability to predict injury severity is expected to speed emergency response and help ensure that the proper personnel and equipment are dispatched to the scene.
“The goal of all of this is to increase the odds for a better outcome for the victims of a crash,” said Laurie Flaherty, program analyst for the National Highway Traffic Safety Administration’s Office of Emergency Medical Services.
To do that, the NHTSA worked with the Centers for Disease Control and Prevention to create vehicle emergency data sets that take into account key aspects of a crash. These include where the impact occurred on the vehicle, whether passengers were wearing seatbelts, whether the vehicle rolled over and delta velocity that indicates the change in velocity of the vehicle as a result of the crash — “which is a lot more complicated than how fast the car was going,” and a better predictive factor, Flaherty said.
Obviously, the more of these factors that are in play, the greater the chance that the crash victims have suffered life-threatening injuries. Indeed, for each factor added to the scenario, the risk factor jumps significantly, explained Gary Wallace, vice president of corporate relations for Irving, Texas-based ATX Group.
“Let’s say there’s a delta velocity of 35 mph, the person was using their safety belt, there were no multiple impacts and no rollover. In that case, there would be a 20% risk of a serious injury,” Wallace said. “Now, if you change just one factor — no seatbelts — the risk factor goes up to 38%. Add another factor and it climbs to 56%.”
While emergency medical technicians routinely perform field triage, it often takes them a long time to reach a crash site, particularly in rural areas. But if dispatchers can use ACN data — which has been available since 1997 — to predict the severity of the injuries suffered by a crash victim, they could get a Medevac helicopter into the air immediately. Or a dispatcher could alert a trauma center that a victim with life-threatening injuries will be heading their way so that treatment can begin faster. On a more basic level, the data could be used to determine, while the ambulance is en route to the scene, whether victims should be transported to a trauma center rather than the nearest hospital.
“If you’re in need of an operating room, this can help them identify you and get you there faster,” Flaherty said.
All of this will improve dramatically the victim’s chance of survival. Typically, victims have a much better chance of recovering if they are treated within a short period of time, the so-called Golden Hour.
“ACN data will help us decide who should respond and which equipment should be sent,” Flaherty said. “That’s important. There was a study that came out in 2006 that showed if you are seriously injured and sent to a Level-1 trauma center, you have a 25% better chance of survival than if you were sent to the local hospital. That’s huge.”
APCO and the National Emergency Number Association formed a joint working group to standardize how public-safety answering points will use ACN data in the future. That work is expected to be completed by the end of the year. Meanwhile, a pilot program will get underway soon in Miami-Dade County, Fla., to test the verity of the algorithms used to predict injury severity from the data, Wallace said.
