How AI Is Improving Emergency Response
Most workflows can benefit from artificial intelligence (AI), but optimization matters more in some roles than others. Emergency response is a prime example — here, efficiency and accuracy improvements save lives.
Emergency services must be fast, accurate, and organize a lot of moving parts effectively. Meeting all those demands is difficult for humans, but AI excels at such work, making it a natural fit for this environment. Here’s how teams worldwide are implementing AI to capitalize on that potential.
1. Reducing Call Center Volumes
AI’s role starts when someone contacts 911. Dispatchers receive massive amounts of calls, but not all require immediate attention. Some are questions about the weather, pranks, and other nonemergencies taking vital time and resources away from real issues. AI can help by fielding communication first.
Several cities have started using natural language processing (NLP) to recognize and divert noncritical calls. After embracing NLP technology, Monterey County, California, saw call volumes drop by 30% — resulting in a 10% boost in efficiency.
NLP can analyze word choice and tone of voice in real time to distinguish between crucial issues and other communication faster and more accurately than people. It can also do so without needing another staff member. Consequently, using chatbots to field 911 calls ensures operators can handle additional cases despite worker shortages.
2. Improving Dispatch Efficiency
Once AI connects people to the appropriate department, it can accelerate the response. Dispatching is a complex process involving contacting multiple teams to direct resources efficiently, which is far from easy. Automating such complicated reactions through machine learning streamlines things.
As dispatchers talk to callers, NLP models can transcribe their words into text to make it easier to inform other parties. The same algorithms can also recognize the most relevant services and contact them automatically. Some can even identify the situation with higher accuracy to ensure people get the help they need. One study found machine learning outperformed dispatchers in recognizing cardiac arrest within the first minute of a phone call.
AI continues to direct resources efficiently once responders are on-site. Mobile emergency response apps include real-time mapping and distance tools to help teams move faster to get where they’re needed. Such assistance also eases decision-making that would otherwise be challenging in high-stress situations.
3. Detecting Emerging Issues
Predictive analytics takes AI’s benefits in the industry further. Because machine learning is so adept at spotting subtle patterns — even achieving 95% accuracy rates in cancer diagnoses — it’s an ideal tool for recognizing emergencies before they’re noticeable to humans.
The California Department of Forestry and Fire Protection has jumped on this potential by using machine vision to detect forest fires. AI monitors feeds from over 1,000 cameras to notice anything outside the usual. When it thinks it sees a fire, it tags the image and alerts relevant authorities so they can address it sooner, minimizing the damage.
4. Streamlining Reporting
Emergency services, like all government operations, also require a lot of paperwork. AI can help by streamlining much of the reporting process to give responders time to focus on critical work.
The same NLP models analyzing 911 dispatchers’ calls can transcribe them in real-time to enter into incident reports. Similar tools can automatically document witness testimonies as they describe what they see. Machine vision models could also summarize what they recognize, adding further analysis to a situation.
Reporting tasks that once took four hours can take just 30 minutes with AI documentation tools. On top of streamlining the process, AI also minimizes errors through automatic transcription and real-time translation, ensuring reports are more reliable.
5. Predicting Future Emergencies
Over time, as more emergency services use AI technologies, they’ll generate additional digital data on the situations they manage. Eventually, they’ll have enough of this information to analyze with predictive analytics to prepare for future emergencies before they happen.
Machine learning models can learn from past events to recognize the early indicators of critical issues. Alternatively, AI analytics could identify trends in such situations to determine what problems will be most prominent in the future. Government agencies can then prepare resources to ensure they can meet these needs effectively.
This use case understandably took off after the COVID-19 pandemic. One AI model successfully predicted the most concerning new COVID variants and could provide similar help in the future. While such tools wouldn’t prevent outbreaks or other emergencies, they could facilitate better responses.
AI Could Revolutionize Emergency Services
AI’s use in emergency response is still new but already showing promising results. As the technology advances, it could help teams worldwide react to emergencies faster and with more thoughtful resource allocation. When that happens, the world will become a safer place.
Originally posted on OpenDataScience.com
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