About two-thirds of emergency room visits by privately insured patients in the United States could be avoided. Many of these visits are related to conditions such as heart failure, COPD, asthma, or muscle and bone problems. These health issues can usually be managed with good outpatient care and patients taking care of themselves. However, problems like missed follow-ups, low patient involvement, and not enough timely care push people to go to the emergency room when they may not need to.
This extra use of emergency rooms causes crowding, raises health costs, and uses up resources that could be saved for more urgent needs. Besides using too many resources, repeated emergency visits can make patients less satisfied and harm their health. With fewer nurses and more paperwork for medical staff, solutions are needed to help manage chronic illnesses better outside emergency rooms.
Large language models, a type of artificial intelligence, help in healthcare by allowing personalized messages and support for doctors’ decisions. They can understand and create human-like text, which lets them talk with patients, read medical data, and help healthcare providers.
Research shows that these models can send messages that fit individual patient needs, helping patients stick to their treatment plans. For example, the company Hinge Health uses these models to send personalized messages based on previous patient talks and health data. Gabriel Mecklenburg, the co-founder of Hinge Health, said that their mix of AI and human help makes messages that improve patient happiness and health results without raising costs.
In managing long-term diseases, these models can:
By automating simple talks and giving quick, caring answers, these models reduce the need for doctors to reach out often while keeping good care. They help manage diseases outside hospitals and can lower avoidable bad health episodes that lead to emergency visits.
Predictive analytics means using data and computer models to guess which patients might have health problems in the future. Using data from electronic health records and social factors, doctors can predict risks. For example, models like the LACE Index and HOSPITAL score help find patients who might return to the hospital soon.
When these tools work together with communication from large language models, doctors can make early plans to help high-risk patients. This might include early follow-ups, checking medicines, and telehealth monitoring to stop diseases from getting worse.
Dr. Ahmad Hassan, who helped research one of the prediction models, says these tools in electronic records help doctors find risky patients without extra work. This helps patients move from hospital to home safely and avoids unnecessary readmissions.
Health systems like Geisinger and Kaiser Permanente have used predictive models to lower avoidable emergency visits and readmissions. At Geisinger, using these models lowered emergency visits for chronic patients by 10%. They also use natural language processing to keep track of patient follow-ups, as in a program managing lung nodules. This example shows how these technologies help manage conditions before they become emergencies.
Even though many patients have access to online portals and digital tools, use of these tools during emergency visits and for chronic illnesses is low in some groups, like males, Black patients, and people without insurance. One study found only 17.4% of emergency patients used digital portals during visits. But people with active accounts are much more likely to use digital health tools.
Tools like mobile apps help patients register before arriving, get updates during visits, and receive digital discharge instructions. These apps can reduce patient stress and help keep care organized. For example, Fabric’s app lets patients track their progress in the emergency room and schedule follow-ups online. These tools also lighten staff work by handling paperwork and letting doctors and nurses focus on care.
Using conversational AI based on large language models, these digital tools can do more. They can gather patient histories, sort symptoms, and check for social problems—all important for personalized care. AI helps patients through their health journey, explains things clearly, and helps reduce the work for medical staff.
Managing chronic diseases well to reduce emergency visits depends on smooth workflows in healthcare. AI tools are becoming important to improve these processes.
In emergency departments, AI tools like Stochastic and Mednition help sort patients by assigning Emergency Severity Index scores. One large study at UCSF found AI was 89% accurate at this. These systems help find high-risk patients early, like those with sepsis, which is often missed at triage. Early alerts from AI lead to faster treatment and better health results.
AI command centers also help move patients faster by predicting crowded times and adjusting staff levels. Less waiting and better flow improve patient experiences and use resources well.
Managing chronic disease includes many routine jobs like follow-ups, sending medicine reminders, writing notes, and teaching patients. AI can automate many of these tasks:
This automation reduces paperwork and supports timely care. For example, at Geisinger, AI tools that assign case managers to high-risk patients before discharge helped lower readmissions and improve care transitions. These efforts are important for value-based care models, where quality and cost matter.
More healthcare providers in the U.S. take part in value-based care programs that focus on good care and controlling costs. AI and large language model technologies fit well with these programs by:
Geisinger Health System’s work in value-based care shows the benefits of combining AI and predictive models. Their STAIR program used AI to improve lung nodule follow-ups, which led to early cancer detection and saved resources.
For AI to work well, it is important to pay attention to how doctors and nurses work. Training and involvement of doctor leaders help reduce resistance. Improved electronic health record workflows and clear AI processes build trust and encourage use.
Even though AI tools can help, differences in digital health use remain a problem. Black patients, males, and underinsured people often use patient portals less, which limits how much they benefit from AI tools.
Also, some prediction models have biases that can underestimate risks for some groups. This can make health inequalities worse. Studies show some healthcare algorithms do not fully consider social factors, which can lead to unfair care.
Healthcare groups must focus on ethical AI use. This includes watching algorithms closely, being open about how AI works, and using data that represents all patient groups. Including patients in AI use plans helps build trust and use.
For healthcare leaders managing outpatient and emergency care, using large language models and AI predictions offers useful benefits:
Selecting and using AI tools should involve teams with doctors, IT workers, and managers. Training and clear AI steps help adoption and make sure technology actually improves results.
The growing abilities of large language models and AI give a useful way to better manage chronic diseases and reduce the load on emergency departments. At the same time, they help patient health across the United States.
This approach to AI in chronic disease management fits healthcare groups aiming to improve care and efficiency as they focus more on sustainable, patient-centered care. By using these tools, medical practices can better meet today’s healthcare needs and help their patients.
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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