How AI-based accident analysis can improve emergency services
- By Itay Bengad
- Mar 12, 2021
The COVID-19 pandemic has made shockingly clear how thin emergency medical services teams have been spread. It’s a reality that many teams endured before the pandemic and it will continue to experience once it’s over. As a result, EMS teams must carefully allocate their emergency response resources to properly address every potential scenario. However, in one such scenario -- the car accident -- first responders face critical operational and technological challenges in dispatching the appropriate resources. Because they have no real-time data from the scene of the accident, EMS teams must depend on witness reports, forcing them to make assumptions when it comes to sending personnel and vehicles to the scene. Not only do these haphazard decisions have implications on the health of the accident victims, but they could cost EMS teams time and money that are already too sparse to be wasted.
Real-time, accurate accident endpoint data can’t be disputed or undervalued
Though EMS teams are exacting medical professionals who use the latest tools and technology, the current method for receiving accident notifications is outdated. EMS teams tend to depend on passersby who witness an accident to call 911 and describe its severity to the emergency dispatchers. What’s more, passersby often deliver their accident accounts under distress, which can lead to exaggeration or errors regarding the state of the victims. Even if police officers arrive at the scene first, they don’t have the medical background to properly report and diagnose the victims’ physical condition. In either case, EMS teams don’t have access to data on what has happened to the victims and can only estimate the resources and personnel necessary to fully address the emergency. What’s more, EMS performance is typically measured by time to the scene. Every additional second or minute spent reaching accident victims could have serious implications in terms of the severity of injuries, and EMS professionals know well that this is precious time that cannot be wasted.
If EMS teams were provided complementary access to an artificial intelligence-based injury analysis system that pulls and analyzes data from a vehicle’s safety systems to ascertain a driver’s likely injuries following a collision, accident notifications could occur autonomously and automatically, without requiring the intervention of the victims or passersby. For instance, with data from advanced accelerometer sensors that precisely detect the forces applied to a vehicle, EMS dispatching teams can accurately allocate their resources according to the severity of damages. This unprecedented on-scene indication of damages can furthermore provide an indication passengers’ bodily injuries so that EMS teams know if they should dispatch a basic, advanced or mortuary ambulance to the scene. Prior knowledge of the severity of the accident and the injuries sustained by victims not only has the potential to improve EMS teams’ key performance indicator of time to the scene, but it can save critically scarce resources by enabling data-driven precision dispatching.
Access to endpoint data allows EMS teams optimize their lifesaving potential
Another benefit of endpoint data delivered directly from the scene is that EMS may actually be able to improve delivery of their principal value proposition – lifesaving potential. Accurate data on vehicle damages and passengers’ injuries allows EMS teams to plan the actions they should take once they reach the scene, including making decisions on the correct hospitals or medical facilities for treating victims’ injuries. With access to this data, delivered not only to dispatching staff but also to EMS professionals’ portable communication devices in the form of anonymized injury reports, medics can let hospitals know they will be arriving, ensuring that emergency room staff and specialists are ready to accommodate patients. They can also provide hospital teams with an initial indication of the injuries so that critical seconds and minutes can be saved to provide optimal treatment.
Despite advances in medical and communication technologies, one of the top medical errors remains avoidable delay in treatment -- a problem that is effectively addressed with accurate endpoint data from the accident. With this solution, EMS teams finally know exactly what needs to be done before they ever step foot on the scene.
EMS teams are the lifesaving force of our society, but it’s a sobering thought that the medical professionals arriving at accident scenes may not be as prepared as they could be because they aren’t provided access to the necessary data points in time. Now, thanks to through innovative in-vehicle sensors that deliver endpoint data in real-time from the scene of an accident, EMS teams are no longer left in the dark when it comes to providing accident victims with optimal lifesaving potential.
Itay Bengad, MD, is CEO of MDgo.