MindTitan developed an AI for Emergency Response Center

The project aims to determine whether and how artificial intelligence can help make risk assessment decisions faster and save valuable time for the emergency contact center. 

The Estonian Emergency Response Center is a government agency that is the first line of help in an emergency that requires the police, ambulance or a rescue unit. In case of emergencies, the Center directs the ambulance or rescue workers to those in need. The Center handles about a million calls per year.

The current problem the Center is facing is that the emergency notification process is very extensive and complex. This means that a lot of valuable time is taken up during the risk assessment by the dispatchers trying to understand the situation.

The challenge in providing a risk assessment lies in the fact that during an emergency call, the dispatcher must understand the situation as soon as possible while taking into account the information needs of at least three different authorities. Gathering such information in limited time and with limited resources is difficult and time-consuming.

This is where artificial intelligence could help. According to the Center, the primary goal is to see if it is possible to create a prototype model using AI that could identify what kind of incident has happened, how serious this incident is, and help the dispatcher identify if it is necessary to ask for additional information to make sure that the most appropriate help is sent.

From the technical aspect, AI will listen to and transcribe a real-time emergency call while taking into account the specifics of a call, like an environment noise, caller anxiety or diction, intermittent call quality, etc. The AI solution listens to and analyses the calls and makes suggestions for dispatchers but does not interfere directly with them, so nothing will change for the caller. The final decision will still be made by the dispatcher in terms of the type and priority of the event.

The solution will improve over time through machine learning, and ultimately reduce errors, and enable to save valuable time in aiding to send help faster.