Public safety answering points (PSAPs) — also known as 911 call centers — provide a critical service. While most people hope never to need one, they are essential to society. Since they are so important, it is reasonable to think they are some of the most efficient institutions in the country. However, that assumption could not be further from the truth. Many emergency communication centers are underfunded and understaffed, resulting in unanswered 911 calls and lengthy dispatch times. Those delays can cost lives. While the clear solution is hiring more people, urban and rural centers have trouble finding candidates. Could artificial intelligence solve this seemingly unsolvable problem? 1. Handles Nonemergency Calls Even though most people are taught from a young age that 911 is for emergencies only, PSAPs receive an incredible number of nonemergency calls. Factoring in spam and misdials, call-takers spend an inordinate amount of time handling situations that don’t matter. The director of government affairs for the International Academies of Emergency Dispatch (IAED) said using AI for nonemergency calls is a fantastic idea and a huge benefit because it enables dispatchers to focus on the calls that really matter. If a machine learning model picks up instead of them, they don’t have to waste their time on noncritical situations. AI can identify spam calls, resolve misdials and route nonemergency calls to the proper agencies, reducing the volume of calls on the administrative line. For example, if someone calls about a loose dog, it can transfer them to animal control. Already, 911 call centers have deployed AI this way. For instance, Charleston County’s Consolidated Emergency Communications Center — whichreceives 210,000 nonemergency calls annually on average — used machine learning to automate its nonemergency line fully. The center’s director said it helps dispatchers provide better assistance. 2. Creates Transcripts of Calls Generative models can automatically create written records of audio communications. These transcripts can be used for reference during dispatch or later training. Since all 911 calls are already recorded, implementation would be relatively straightforward. Automatic transcription and summarization would be a massive help during frantic conversations. What has happened, who needs help and where the crisis is unfolding are the most important pieces of data in an emergency. AI can help dispatchers understand the situation’s scope and collect their thoughts, improving decision-making efficiency. 3. Conducts Sentiment Analysis A machine learning model can leverage natural language processing to conduct sentiment analysis, allowing it to identify and react to mood. This technology can increase dispatch efficiency when deployed in emergency services systems. While dispatchers are trained to respond to an array of crises, every person who calls 911 reacts to danger differently. The algorithm can detect these subtle differences, enabling helpful tone, wording and intervention recommendations. 4. Reviews Dispatch Data for Accuracy Many 911 dispatchers are overworked. One investigation into the D.C. Office of Unified Communications (OUC) payroll revealed that almost 40% of employees’ hours are overtime. Many work 12-hour shifts — sometimes multiple in one week. Long hours and sleepless nights lead to human error. Call-takers may send first responders to the wrong address or leave a caller on hold for minutes. In a field where every second counts, these delays are unacceptable. Besides, forced overtime will eventually result in burnout, compounding the sector’s labor shortage. AI can automate repetitive job functions and analyze information before relaying it, lightening the workload and ensuring accuracy. In addition to increasing efficiency, it can minimize human error, helping save lives. Embedding AI into emergency vehicles helps dispatchers retain control of the situation while they remain on the line with callers. It can relay critical information, provide accurate arrival time estimates and even automate some driving functions, helping teams focus on what matters. On top of improving efficiency, it could make driving safer. Research shows incorporating forward collision warning and autonomous emergency braking systems couldreduce rear-end collisions by 50%, saving 850 lives annually. While fire truck and ambulance accidents are uncommon, they happen. Preventing them saves precious time. 5. Provides Training for all Situations While 911 call center operators receive extensive training, many don’t feel prepared to handle all possible situations. Just because they’re used to burglaries and trespassing doesn’t mean they know how to respond to a bomb threat or an active shooter. According to a National Emergency Number Association survey, under 50% of emergency services professionals feel equipped to handle any situation. They are also ill-prepared to deal with the administrative side of their work. Around 36% agreed management was the most stressful aspect of their job, ranking it above taking incoming 911 calls. A machine learning model can create personalized training material to maximize improvements, helping dispatchers excel in areas where they previously lagged behind. Also, it can help establish individualized workflows to improve their management and administrative capabilities. 6. Handles Incident-Specific Call Surges During events that cause mass panic, PSAPs are flooded with 911 calls. Those that are understaffed due to retention and hiring issues — which is most of them — will have trouble helping everyone. A nationwide staffing survey by IAED revealed that 911 call center vacancy rates averaged 25% in 2022, meaning one in four positions was unfilled. This figure represents a five-percentage-point increase from before the COVID-19 pandemic. Even if dispatchers can handle their regular call volume without trouble, these unexpected situations can easily cause prioritization and management issues. A machine learning model can handle incoming calls, freeing up their time. Since this technology can evolve, it can adapt to never-before-seen crises. AI can relay relevant, helpful information to callers to ensure everyone receives help. This is especially helpful for people who aren’t connected to mass panic events because it ensures they receive a timely response. 7. Responds to Text-to-911 Messages The ability to reach 911 operators from a mobile device is called the text-to-911 service. It is increasing in popularity. According to the National 911 Program, people in 38 states sent 507,969 texts to PSAPs in 2021, up from 492,328 in 2020. While this program could be revolutionary, it poses some challenges. What happens when the sender misspells a word or types slowly? AI can increase efficiency by generating response prompts, reading the message aloud or fixing spelling mistakes. Contrary to popular belief, most people won’t be put off by receiving AI-generated texts. Research shows they perceive this content as better quality than work produced by human experts. AI Can Make Every Second Count in 911 Call Centers AI will never replace human operators. Instead, it will enhance their jobs, increasing their efficiency and narrowing their focus so they can focus on what matters. This technology could be revolutionary for the nation’s vast number of understaffed, underfunded PSAPs, helping save lives and improve citizens’ perception of local emergency services. Public safety answering points (PSAPs) — also known as 911 call centers — provide a critical service. While most people hope never to need one, they are essential to society. Since they are so important, it is reasonable to think they are some of the most efficient institutions in the country. However, that assumption could not be further from the truth. Public safety answering points (PSAPs) — also known as 911 call centers — provide a critical service. While most people hope never to need one, they are essential to society. Since they are so important, it is reasonable to think they are some of the most efficient institutions in the country. However, that assumption could not be further from the truth. Many emergency communication centers are underfunded and understaffed, resulting in unanswered 911 calls and lengthy dispatch times. Those delays can cost lives. While the clear solution is hiring more people, urban and rural centers have trouble finding candidates. Could artificial intelligence solve this seemingly unsolvable problem? 1. Handles Nonemergency Calls Even though most people are taught from a young age that 911 is for emergencies only, PSAPs receive an incredible number of nonemergency calls. Factoring in spam and misdials, call-takers spend an inordinate amount of time handling situations that don’t matter. The director of government affairs for the International Academies of Emergency Dispatch (IAED) said using AI for nonemergency calls is a fantastic idea and a huge benefit because it enables dispatchers to focus on the calls that really matter. If a machine learning model picks up instead of them, they don’t have to waste their time on noncritical situations. is a fantastic idea is a fantastic idea AI can identify spam calls, resolve misdials and route nonemergency calls to the proper agencies, reducing the volume of calls on the administrative line. For example, if someone calls about a loose dog, it can transfer them to animal control. Already, 911 call centers have deployed AI this way. For instance, Charleston County’s Consolidated Emergency Communications Center — which receives 210,000 nonemergency calls annually on average — used machine learning to automate its nonemergency line fully. The center’s director said it helps dispatchers provide better assistance. receives 210,000 nonemergency calls receives 210,000 nonemergency calls 2. Creates Transcripts of Calls 2. Creates Transcripts of Calls Generative models can automatically create written records of audio communications. These transcripts can be used for reference during dispatch or later training. Since all 911 calls are already recorded, implementation would be relatively straightforward. Automatic transcription and summarization would be a massive help during frantic conversations. What has happened, who needs help and where the crisis is unfolding are the most important pieces of data in an emergency. AI can help dispatchers understand the situation’s scope and collect their thoughts, improving decision-making efficiency. 3. Conducts Sentiment Analysis 3. Conducts Sentiment Analysis A machine learning model can leverage natural language processing to conduct sentiment analysis, allowing it to identify and react to mood. This technology can increase dispatch efficiency when deployed in emergency services systems. can increase dispatch efficiency can increase dispatch efficiency While dispatchers are trained to respond to an array of crises, every person who calls 911 reacts to danger differently. The algorithm can detect these subtle differences, enabling helpful tone, wording and intervention recommendations. 4. Reviews Dispatch Data for Accuracy 4. Reviews Dispatch Data for Accuracy Many 911 dispatchers are overworked. One investigation into the D.C. Office of Unified Communications (OUC) payroll revealed that almost 40% of employees’ hours are overtime. Many work 12-hour shifts — sometimes multiple in one week. 40% of employees’ hours 40% of employees’ hours Long hours and sleepless nights lead to human error. Call-takers may send first responders to the wrong address or leave a caller on hold for minutes. In a field where every second counts, these delays are unacceptable. Besides, forced overtime will eventually result in burnout, compounding the sector’s labor shortage. AI can automate repetitive job functions and analyze information before relaying it, lightening the workload and ensuring accuracy. In addition to increasing efficiency, it can minimize human error, helping save lives. Embedding AI into emergency vehicles helps dispatchers retain control of the situation while they remain on the line with callers. It can relay critical information, provide accurate arrival time estimates and even automate some driving functions, helping teams focus on what matters. On top of improving efficiency, it could make driving safer. Research shows incorporating forward collision warning and autonomous emergency braking systems could reduce rear-end collisions by 50% , saving 850 lives annually. While fire truck and ambulance accidents are uncommon, they happen. Preventing them saves precious time. reduce rear-end collisions by 50% reduce rear-end collisions by 50% 5. Provides Training for all Situations 5. Provides Training for all Situations While 911 call center operators receive extensive training, many don’t feel prepared to handle all possible situations. Just because they’re used to burglaries and trespassing doesn’t mean they know how to respond to a bomb threat or an active shooter. According to a National Emergency Number Association survey, under 50% of emergency services professionals feel equipped to handle any situation. They are also ill-prepared to deal with the administrative side of their work. Around 36% agreed management was the most stressful aspect of their job, ranking it above taking incoming 911 calls. under 50% of emergency services professionals under 50% of emergency services professionals A machine learning model can create personalized training material to maximize improvements, helping dispatchers excel in areas where they previously lagged behind. Also, it can help establish individualized workflows to improve their management and administrative capabilities. 6. Handles Incident-Specific Call Surges 6. Handles Incident-Specific Call Surges During events that cause mass panic, PSAPs are flooded with 911 calls. Those that are understaffed due to retention and hiring issues — which is most of them — will have trouble helping everyone. A nationwide staffing survey by IAED revealed that 911 call center vacancy rates averaged 25% in 2022, meaning one in four positions was unfilled. This figure represents a five-percentage-point increase from before the COVID-19 pandemic. vacancy rates averaged 25% vacancy rates averaged 25% Even if dispatchers can handle their regular call volume without trouble, these unexpected situations can easily cause prioritization and management issues. A machine learning model can handle incoming calls, freeing up their time. Since this technology can evolve, it can adapt to never-before-seen crises. AI can relay relevant, helpful information to callers to ensure everyone receives help. This is especially helpful for people who aren’t connected to mass panic events because it ensures they receive a timely response. 7. Responds to Text-to-911 Messages 7. Responds to Text-to-911 Messages The ability to reach 911 operators from a mobile device is called the text-to-911 service. It is increasing in popularity. According to the National 911 Program, people in 38 states sent 507,969 texts to PSAPs in 2021, up from 492,328 in 2020. While this program could be revolutionary, it poses some challenges. sent 507,969 texts to PSAPs sent 507,969 texts to PSAPs What happens when the sender misspells a word or types slowly? AI can increase efficiency by generating response prompts, reading the message aloud or fixing spelling mistakes. Contrary to popular belief, most people won’t be put off by receiving AI-generated texts. Research shows they perceive this content as better quality than work produced by human experts. perceive this content as better quality perceive this content as better quality AI Can Make Every Second Count in 911 Call Centers AI Can Make Every Second Count in 911 Call Centers AI will never replace human operators. Instead, it will enhance their jobs, increasing their efficiency and narrowing their focus so they can focus on what matters. This technology could be revolutionary for the nation’s vast number of understaffed, underfunded PSAPs, helping save lives and improve citizens’ perception of local emergency services.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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