In many cities in the U.S., many 911 calls and emergency room visits are for problems that do not need emergency care. Research from Baltimore City shows that about 35% of 911 calls are for health issues that are not emergencies. These unnecessary trips strain resources, cost more money, and lead to long waits and overcrowded waiting rooms.
A study of the Mobile Integrated Health Community Paramedicine (MIH-CP) program in Baltimore City found that when EMS paramedics used other care options, fewer patients had to be taken to the emergency room. Out of 1,084 low-acuity calls, only 8.5% of patients needed transport to the ED with the program. Without it, about 65% were transported. This means there was a 23% lower risk of unnecessary trips to the emergency room.
Besides lowering overcrowding in the ED, the program also saved about 23 minutes for each EMS call. This allowed ambulances to get to more serious emergencies faster. The program also showed it could save money if only about three patients a day were enrolled. Similar results have been seen in other states, where nurse practitioners and physician assistants help in triage and decision making.
Using AI in EMS and emergency call centers can help make triage more accurate and guide patients to the right care. 911-based AI triage uses conversational AI, machine learning, and large language models to listen to symptoms on emergency calls and decide how serious the case is in real-time.
Services like MD Ally and RightSite have made AI tools that check symptoms and risks during 911 calls. This helps dispatchers find low-acuity cases that can get virtual care or go to other care places instead of the ED. When linked with EMS dispatch systems, these AI tools help cut down on unnecessary ambulance rides and emergency visits.
For example, AI systems can suggest urgent care centers, telemedicine visits, or care at home, saving money on ambulance rides and expensive ED treatments. They can also help with prescriptions and transportation for patients who need care but not emergency help.
Studies show that AI-guided triage during 911 calls helps use EMS resources better. Ambulances go faster to serious emergencies. This also lowers crowding in emergency rooms and lets hospital staff focus on patients who need more help.
Many emergency visits happen because of worsening chronic conditions like heart failure, COPD, asthma, and muscle problems. Many patients call EMS even when they could get care some other way that is not an emergency.
AI tools that use large language models are starting to help reduce overuse of emergency resources for chronic illness care. For example, Hinge Health uses AI to send personalized messages to patients to help them stay engaged in their care outside hospitals. Gabriel Mecklenburg, co-founder of Hinge Health, says that these AI messages help care teams communicate better and reduce avoidable emergency visits by giving timely help during regular care.
Combining AI messaging with 911 triage and EMS diversion could reduce emergency room visits by informing and guiding patients earlier in their care path.
Adding AI to EMS and emergency room workflows can make things run more smoothly and improve patient care. AI helps with decisions and paperwork, so healthcare workers can spend more time on patient care.
One example is AI agents that talk to patients during 911 calls or ambulance rides. These agents gather patient history, check symptoms, and fill out electronic health records before the patient arrives at the ED. This cuts down on extra paperwork for nurses and gives doctors accurate information quickly.
Inside emergency rooms, AI tools like Stochastic’s patient acuity classifier can give Emergency Severity Index scores with 89% accuracy, close to what doctors decide. Mednition’s system detects risks like sepsis early, which often is missed during regular triage.
Other AI programs, like Qventus, watch patient flow data to predict crowding, help with staffing, and manage beds to avoid delays.
Getting patients involved through digital tools still has room to grow, especially in emergency rooms. Studies show that only about 17.4% of emergency patients use patient portals. Use is even lower among men, Black patients, and those without commercial insurance.
Apps like Fabric help by letting patients register before arriving, track their visit in real time, and speed up discharge. These apps also have quick assessment tools and help schedule follow-up appointments. These features help patients understand and manage their care better.
In the future, AI agents could guide patients through their emergency visits—from before arrival to discharge—by answering questions in simple language and checking for social factors affecting health. This could also help lower the workload for nurses, especially when staff are short.
Even with good results, adding AI to EMS and emergency rooms has challenges. One problem is undertriaging, where serious cases might be mistaken for less serious ones. To keep patients safe, AI tools need a lot of testing, ongoing monitoring, and human review.
Another issue is making sure AI works fairly for all groups. Some people, especially in underserved or minority communities, use digital tools less. This could reduce the benefits of AI virtual care. EMS and AI triage programs should be fair and use communication that respects different cultures.
Money is also a factor. The end of CMS’s ET3 payment model, which paid for alternative care options, shows that funding AI and EMS changes can be hard to keep going.
Healthcare leaders and IT managers can use 911-based AI triage to improve operations and patient care in several ways:
Healthcare groups thinking about these tools should carefully assess what they need, involve all users, and set up strong data security and privacy.
Using 911-based AI triage with EMS diversion and virtual care options can help U.S. healthcare systems improve emergency care. Medical leaders and IT managers are important in using these tools to change workflows and make sure care is fair and timely. As more people need emergency services, AI tools offer a way to get better results and support healthcare systems.
Pre-ED triage helps reduce unnecessary emergency department (ED) visits by guiding patients to the appropriate level of care using AI chatbots and 911-integrated triage services. It enhances patient decision-making and system efficiency by diverting low-acuity cases to virtual or home-based care, thus lowering healthcare costs and avoiding ED overcrowding.
911-integrated triage services like MD Ally and RightSite assess the severity of conditions during emergency calls and redirect low-acuity cases to virtual care options. They provide additional support like prescription assistance or transportation, helping to reduce avoidable ED visits and EMS usage, while aligning incentives between payers and emergency services.
LLMs enable personalized messaging and communication that improve patient engagement and clinical outcomes for ambulatory-sensitive conditions (ASCs) such as heart failure or COPD. Startups like Hinge Health use LLMs to tailor interactions and reduce unnecessary ED visits by managing chronic illnesses effectively outside hospital settings.
AI tools like Stochastic and Mednition support clinical decision-making by accurately classifying patient acuity and identifying high-risk patients early, improving resource allocation. AI-driven command centers optimize throughput, predict crowding, and balance staffing, easing bottlenecks to maintain efficient patient flow and timely care delivery.
LLMs can track patient progress against clinical guidelines in real time, flag delays (e.g., missing tests), and prioritize care. This granular patient-level monitoring can accelerate appropriate discharges and optimize bed management beyond operational metrics, improving adherence to care standards and reducing crowding.
Apps like Fabric engage patients before and during ED visits by enabling pre-registration, providing visit progress updates, and offering digital discharge processes. These tools reduce documentation burden on staff, improve patient navigation, and decrease the rate of patients leaving before being seen, thereby improving care continuity and satisfaction.
Conversational AI agents can collect patient history, triage severity, pre-populate clinical notes, screen for social determinants of health, and guide patients through their ED stay in understandable terms. This reduces nurse workload, shortens wait times, and enhances communication, supporting better patient engagement and streamlined workflows.
Viz.ai uses deep learning to analyze imaging (CT, ECG) for rapid stroke and vascular care decisions, reducing treatment time. Heartflow assesses cardiac blood flow noninvasively via AI-driven CT analysis to avoid invasive procedures and expedite chest pain patient discharge, enhancing safety and efficiency in ED triage.
Unlike 911 triage solutions where ED diversions are clearly measurable, digital front door tools face complex attribution challenges as they need to demonstrate impact on patient behavior and healthcare utilization earlier in the care journey, requiring alignment of incentives across stakeholders and longitudinal outcome tracking.
Studies show low patient portal usage during ED visits, especially among males, Black patients, and uninsured populations, which limits the benefits of digital tools. Promoting equitable access to digital engagement before and during ED visits enhances participation, improves communication, and supports better health outcomes across diverse patient groups.
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