Artificial Intelligence (AI), and specifically Machine Learning (ML), are being tested in an increasing number of fields, including data-centric environments. Image or text analysis, speech recognition, chatbot interactions, custom machine learning models… all these are elements that could enable the AI journey of a public safety and security organisation.

But can Artificial Intelligence help save more lives? We believe that AI has the potential to enable significant savings compared to current methods employed by the emergency medical services sector, as well as offer added benefits of automated analytics to measure dispatch quality and continuous training of human operators.

Table of contents
  • AI & emergency services
  • EENA AI project
  • Useful resources

ai & emergency services

In the context of an emergency, where increased physiological and environmental pressure as well as many other factors interact every second, we must make sure that we use all of our resources in the most efficient way to obtain the best possible outcome. Machines can process huge amounts of data faster that humans can, and also learn to constantly improve.

How can Artificial Intelligence benefit the work of emergency services?

• It can reduce repetitive and procedural activities currently done by people. These solutions called RPA (Robotic Process Automation) have already been applied in different areas like finance, procurement, and personnel services/human resources
• It can process huge amount, finding patterns and new insights. This can help in video and voice recognition which was normally done by people.
• AI and ML can assist in cybersecurity and monitoring cyberspace, such as fraud detection and helping to find information on the various layers of the web.
• With the broad expansion of IoT devices and the opportunity to integrate those sensors into the PSAP; better sensor integration, coupled with edge analytics, can increase the visibility and understanding (situational awareness) on what is occurring.
• …etc

Find out how public safety organisations and emergency services are making the most of AI and ML in our document “Artificial Intelligence & Machine Learning in Public Safety” :

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eena ai project

That is why EENA launched in May 2018 a year-long joint initiative with Corti to pilot AI support to improve emergency medical service operations.The initiative was launched in two European countries and will provide real-time decision support for emergency dispatchers responding to cardiac arrests, effectively enhancing human decision-making skills with AI.

How does this work? A real-time automatic speech recognition technology provides recommendations and advice for medical dispatchers handling emergency calls. During the call, the data is analyzed and compared with historical data collected from millions of previous emergency calls, which is how the platform learns over time. As understanding of the incident increases during the call, achieved both through the analyzed data and the caller’s input, Corti will learn to predict the criticality of the situation, delivering real-time alerts and recommendations to the dispatcher.

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useful resources