For many years now, computer-aided dispatch (CAD) systems have been an important part of emergency response protocols. These systems are designed to help operators ensure they’re utilizing the right resources for the job and sending appropriate EMS teams to optimal locations.

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However, with new advancements in artificial intelligence (AI) technology, there are now much faster, more efficient systems being developed and actively incorporated into response procedures. 

New AI-powered public safety solutions are helping to utilize emergency response resources in more effective ways while supporting better situational awareness for both dispatch teams and first responders on the ground.

AI-powered Voice Recognition and Natural Language Processing

The traditional format for answering and responding to 911 emergencies has been historically reliant on human dispatchers using their individual training to interpret the information being provided and make decisions on how to dispatch appropriate emergency services.

However, while the training these operators receive is highly regulated and continuously reinforced, the fact is that there is always room for error in human operations. This is where AI-powered dispatch systems can add tremendous value.

Using advanced voice recognition technology paired with natural language processing capabilities, 911 operating centers are able to gather and act on information in real time. This includes recognizing the nuances of specific call dialogues and even the tone of the caller to help categorize the severity of the situation. 

AI-driven or supported systems also help significantly reduce the time spent sending appropriate resources to the right locations by being able to decipher critical information about the emergency, including the location details, nature of the incident, and any parties involved. This can save precious seconds or minutes when responding to a situation and greatly improve response times while reducing the margin for data entry errors.

Better Resource Allocation and Prioritization

As we look forward to the next generation of AI-driven CAD systems, there is promising to be a significant leap forward when it comes to resource optimization and 911 call prioritization. Next Generation 911 (NG911) is one example of how dispatch teams can use multiple information formats to greatly improve their accuracy.

For example, when utilizing advanced AI-support systems, dispatch teams can reroute responders in real time based on traffic conditions being reported through a complex network of IoT (Internet of Things) technology sensors. This, along with GPS tracking devices, can identify the closest available responders in an area while dynamically prioritizing teams closest to emergency scenes.

These advanced solutions also help to ensure that all calls are prioritized correctly while avoiding a static queue – allowing situations to be updated in real time with up-to-date information. This can ensure that as cases escalate or deescalate in the field, responders are able to successfully reroute as needed and put their time and resources where it is most needed at any given time.

Predictive Analytics for More Efficient Dispatch

One of the most exciting possibilities that AI technology is bringing to emergency services is ability to apply predictive analytics to procedure management. Because AI tools are able to collect and analyze huge data sources in near real-time, they’re also able to provide incredibly detailed reports with revealing insights.

Over time, these tools can begin compiling information related to trends or patterns being identified regarding emergency response locations and the nature and severity of the issues. This can be incredibly valuable when predicting future “hotspot” locations for various emergency incidents and can help EMS teams ensure they have the right tools and equipment to respond properly.

While most emergency response units have a variety of tools at their disposal, when called into action, there may be extended delays to responses or the need to be transported to a local hospital if they don’t have everything they need for situations they’re presented with. Predictive models can help to minimize or even eliminate the likelihood of this situation happening.

Improved Situational Awareness and Communication

Situational awareness is so important when it comes to responding to an emergency. If first responders aren’t provided with enough information or the details are inconsistent, it can cost someone their life.

In this case, it’s important for EMS teams to have access to real-time data rather than having the information pass through several lines of communication. AI-supported technologies provide this level of detail to support teams while giving them a complete, unaltered picture of the scene before they arrive.

Also, in areas where there are various populations made up of diverse language groups, AI-powered CAD systems can help to systematically bridge the communication gap – translating caller dialogues with incredible accuracy and explaining the emergency.

Looking Ahead

As we continue looking forward, AI technology will continue to support the way we live in multiple ways, including the level of support we receive from emergency services. Although AI in itself will never be able to replace the importance of human perspective and judgment when it comes to making life-saving decisions, it can be a valuable tool that can help save the precious seconds first response teams need to do their jobs effectively.

Author bio

Kevin Ruef co-founded 10-8 Systems after exceeding multiple companies’ sales records (both domestically and internationally). With more than a decade in sales, his experience ranges from B2B, B2G, and B2C. Since the company’s start in 2019, Kevin has been responsible for business development, strategic partnerships, and business operations.