Kevin Ruef, Co-Founder of 10-8 Systems delves into the evolution of computer-aided dispatch systems with AI and machine learning.
In dispatch, the quality of communication between dispatchers, callers and first responders can mean the difference between life and death.
It may be human error in some cases, but technology offers an opportunity to transform computer-aided dispatch (CAD) and save lives.
As of 2021, the CAD market was $1.73 billion. By 2028, it’s projected to grow to $3.95 billion – a 12.5% CAGR with North America as the highest-growing market.
Part of this growth trajectory is the emergence of connected AI and machine learning, bolstering CAD capabilities.
CAD systems have been integral to public safety since the early 1970s. Early CAD programs were developed in the 1960s, but they didn’t gain widespread use until law enforcement agencies adopted them in the 1990s.
Along with CAD, Amber Alerts for missing children and protocols for people with hearing impairments were also developed.
All of these capabilities wouldn’t have been possible without computers and wireless phones.
Now, CAD is remarkably complex.
It can predict service needs, track availability, recommend route changes to optimize response times, transfer data and more.
Prior to technological advances, these functions required teams of people working long hours.
Ultimately, CAD systems reduced the number of people necessary to disseminate information to a large number of recipients.
In addition, they increased the volume of information and delivered it at a faster rate, ensuring first responders knew where they needed to go, got there quickly, and arrived prepared for what they’d find.
CAD systems have been indispensable to public safety and emergency response and their capabilities are only growing.
CAD systems can encompass a lot of different functions, including fielding calls, dispatching, location verification, resource management, call disposition, and status management.
But with AI and machine learning, the possibilities are virtually endless.
Here are some changes on the leading edge:
Audio is unstructured data – it needs to be arranged and analyzed to be usable.
This can be challenging, but advancements in technology enable audio analysis for deep learning.
For example, deep learning and audio analytics can analyze a 911 caller’s words and emotional state to inform first responders.
This is helpful for not only monitoring compliance and reducing liability, but allowing the dispatchers to pick up concerning phrases or connect disparate calls to get a full picture of the situation.
CAD technologies rely on data through coordinated systems, which can cause gaps in information or other inefficiencies.
Currently, nearby responders are found using computerized mapping that considers the vehicle location, automatic number identification and caller identification.
With new CAD systems, such as Next Generation 911, it’s possible to incorporate robust GIS technology with advanced analytics and data visualization to find the most efficient routes and optimize resource allocation.
Silent dispatching, or automated voice-free communication between dispatchers and first responders, has been in testing since 2015.
The goals with silent dispatching were to cut radio traffic, reduce the data entry workload, decrease nuisance calls, or protect callers in dangerous situations.
Despite its promise, silent dispatching has yet to catch on.
However, Uber is testing out a silent “panic button” for its vehicles to give passengers a way to alert authorities if they’re in an unsafe situation.
As that rolls out, silent dispatching with CAD may see wider adoption and applications across departments.
Wearable technology has been making waves in the healthcare industry.
Devices like LifeAlert are already on the market, but CAD may hold possibilities for integration in the future.
Wearable technology hasn’t become popular in the consumer market, but new features are gaining traction that may be applicable to first responders.
Apple recently announced a walkie-talkie feature for the Apple Watch, which may have uses for healthcare and first response.
For example, a wearable device that can access medical records or send images can inform the first responders of the scenario before they arrive at the scene.
Computer-aided drone dispatch offers what is perhaps the most promising of applications.
Drones are already in wide use, but with CAD technology, it’s possible to surveil the spread of wildfires, monitor long stretches of rugged terrain, or deliver life-saving medical devices to people who need them.
This is especially important for rescue response in remote areas that make medical assistance challenging.
Wearables may not have caught on, but voice assistants have.
Amazon Echo, Google Home, and Apple HomePods have found their way into many homes, offering everything from quick search queries to reciting recipes to making calls or texts to contact lists.
With CAD integration, voice assistants can do even more.
For example, a smart speaker may be able to assist in an emergency by providing dispatcher instructions like CPR or reminding patients or caretakers to provide medication according to a schedule.
Voice assistants with CAD can also trigger 911 calls for a variety of emergencies, such as high detected carbon monoxide levels or an intruder alert.
The growth and evolution may have been slow and steady so far, but emerging technologies like AI and machine learning offer incredible opportunities for CAD to become even more efficient for emergency providers and their patients.
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