Emergency departments (EDs) in the U.S. have seen big jumps in patient visits and call numbers over the last ten years. In 2014, there were about 141 million ED visits across the country, and the number keeps growing faster than the population. Many calls and visits are for cases that are not urgent or emergencies, which adds extra pressure on medical staff and resources.<\/p>\n
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In states like California, Medicaid patients make up a large part of ED visits. This creates a complicated need for emergency services that call centers must handle well. When calls are high, wait times get longer, ambulance dispatch can be delayed, and call center staff can get overwhelmed.<\/p>\n
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In this situation, triage\u2014the process of putting patients in order based on how urgent their situation is\u2014becomes even more important but harder to do by hand.<\/p>\n
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Artificial Intelligence in emergency call centers usually includes voice recognition, natural language processing (NLP), machine learning (ML), and sometimes predictive analytics. These tools work together to analyze calls, understand patient conditions from what they say, and decide how urgent the case is.<\/p>\n
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For example, Simbo AI uses AI answering systems that can handle a large part of emergency calls automatically. In Monterey County, California, Simbo AI\u2019s phone agent managed nearly 30% of emergency calls in one month. It handled 2,920 out of 9,635 calls. This improvement made the call center 7-10% more efficient and let human dispatchers focus on the most critical cases.<\/p>\n
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Other AI platforms, like the UK’s National Health Service’s Corti.AI system, can detect cardiac arrests outside of hospitals within the first minute of a call 36% of the time. These early alerts allow faster ambulance responses and can improve chances of survival.<\/p>\n
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AI systems listen to call details in real-time, suggest questions for call operators, and help with better patient triage decisions. By checking symptoms, patient history, and important details during the call, AI helps call center staff use resources more effectively.<\/p>\n
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