AI Outperforms Nurses in Predicting Hospital Admissions, Promising to Reduce ER Overcrowding

Artificial intelligence predicts hospital admissions hours earlier in emergency departments

Mount Sinai Health SystemAug 11 2025 Artificial intelligence (AI) can help emergency department (ED) teams better anticipate which patients will need hospital admission, hours earlier than is currently possible, according to a multi-hospital study by the Mount Sinai Health System. By giving clinicians advance notice, this approach may enhance patient care and the patient experience, reduce overcrowding and "boarding" (when a patient is admitted but remains in the ED because no bed is available), and enable hospitals to direct resources where they're needed most. Among the largest prospective evaluations of AI in the emergency setting to date, the study published in the July 9 online issue of the journal Mayo Clinic Proceedings: Digital Health [https://doi.org/10.1016/j.mcpdig.2025.100249]. In the study, researchers collaborated with more than 500 ED nurses across the seven-hospital Health System. Together, they evaluated a machine learning model trained on data from more than 1 million past patient visits. Over two months, they compared AI-generated predictions with nurses' triage assessments to see whether AI could help identify likely hospital admissions sooner after the patient arrives. Emergency department overcrowding and boarding have become a national crisis, affecting everything from patient outcomes to financial performance. Industries like airlines and hotels use bookings to forecast demand and plan. In the ED, we don't have reservations. Could you imagine airlines and hotels without reservations, solely forecasting and planning from historical trends? Welcome to health care. Our goal was to see if AI combined with input from our nurses, could help hasten admission planning, a reservation of sorts. We developed a tool to forecast admissions needs before an order is placed, offering insights that could fundamentally improve how hospitals manage patient flow, leading to better outcomes." Jonathan Nover, MBA, RN, Lead Author, Vice President of Nursing and Emergency Services, Mount Sinai Health System The study, involving nearly 50,000 patient visits across Mount Sinai's urban and suburban hospitals, showed that the AI model performed reliably across these diverse hospital settings. Surprisingly, the researchers found that combining human and machine predictions did not significantly boost accuracy, indicating that the AI system alone was a strong predictor. "We wanted to design a model that doesn't just perform well in theory but can actually support decision-making on the front lines of care," says co-corresponding senior author Eyal Klang, MD, Chief of Generative AI in the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai. "By training the algorithm on more than a million patient visits, we aimed to capture meaningful patterns that could help anticipate admissions earlier than traditional methods. The strength of this approach is its ability to turn complex data into timely, actionable insights for clinical teams-freeing them up to focus less on logistics and more on delivering the personal, compassionate care that only humans can provide." While the study was limited to one health system over a two-month period, the team hopes the findings will serve as a springboard for future live clinical testing. The next phase involves implementing the AI model into real-time workflows and measuring outcomes such as reduced boarding times, improved patient flow, and operational efficiency. "We were encouraged to see that AI could stand on its own in making complex predictions. But just as important, this study highlights the vital role of our nurses-more than 500 participated directly-demonstrating how human expertise and machine learning can work hand in hand to reimagine care delivery," says co-corresponding senior author Robbie Freeman, DNP, RN, NE-BC3, Chief Digital Transformation Officer at Mount Sinai Health System. "This tool isn't about replacing clinicians; it's about supporting them. By predicting admissions earlier, we can give care teams the time they need to plan, coordinate, and ultimately provide better, more compassionate care. It's inspiring to see AI emerge not as a futuristic idea, but as a practical, real-world solution shaped by the people delivering care every day." The paper is titled "Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System." The study's authors, as listed in the journal, are Jonathan Nover, MBA, RN; Matthew Bai, MD; Prem Tismina; Ganesh Raut; Dhavalkumar Patel; Girish N Nadkarni, MD, MPH; Benjamin S. Abella, MD, MPhil; Eyal Klang, MD, and Robert Freeman, DNP, RN, NE-BC3. This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. The research was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. Mount Sinai Health System Journal reference: Nover, J., et al. (2025). Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System. Mayo Clinic Proceedings: Digital Health. doi.org/10.1016/j.mcpdig.2025.100249.

AI could help emergency rooms predict admissions, driving more timely, effective care

Artificial intelligence (AI) can help emergency department (ED) teams better anticipate which patients will need hospital admission, hours earlier than is currently possible, according to a multi-hospital study by the Mount Sinai Health System. By giving clinicians advance notice, this approach may enhance patient care and the patient experience, reduce overcrowding and "boarding" (when a patient is admitted but remains in the ED because no bed is available), and enable hospitals to direct resources where they're needed most. Among the largest prospective evaluations of AI in the emergency setting to date is the study published in the July 9 online issue of the journal Mayo Clinic Proceedings: Digital Health. In the study, researchers collaborated with more than 500 ED nurses across the seven-hospital Health System. Together, they evaluated a machine learning model trained on data from more than 1 million past patient visits. Over two months, they compared AI-generated predictions with nurses' triage assessments to see whether AI could help identify likely hospital admissions sooner after the patient arrives. "Emergency department overcrowding and boarding have become a national crisis, affecting everything from patient outcomes to financial performance. Industries like airlines and hotels use bookings to forecast demand and plan. In the ED, we don't have reservations. Could you imagine airlines and hotels without reservations, solely forecasting and planning from historical trends? Welcome to health care," says lead author Jonathan Nover, MBA, RN, Vice President of Nursing and Emergency Services, Mount Sinai Health System. "Our goal was to see if AI combined with input from our nurses, could help hasten admission planning, a reservation of sorts. We developed a tool to forecast admissions needs before an order is placed, offering insights that could fundamentally improve how hospitals manage patient flow, leading to better outcomes." The study, involving nearly 50,000 patient visits across Mount Sinai's urban and suburban hospitals, showed that the AI model performed reliably across these diverse hospital settings. Surprisingly, the researchers found that combining human and machine predictions did not significantly boost accuracy, indicating that the AI system alone was a strong predictor. "We wanted to design a model that doesn't just perform well in theory but can actually support decision-making on the front lines of care," says co-corresponding senior author Eyal Klang, MD, Chief of Generative AI in the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai. "By training the algorithm on more than a million patient visits, we aimed to capture meaningful patterns that could help anticipate admissions earlier than traditional methods. The strength of this approach is its ability to turn complex data into timely, actionable insights for clinical teams -- freeing them up to focus less on logistics and more on delivering the personal, compassionate care that only humans can provide." While the study was limited to one health system over a two-month period, the team hopes the findings will serve as a springboard for future live clinical testing. The next phase involves implementing the AI model into real-time workflows and measuring outcomes such as reduced boarding times, improved patient flow, and operational efficiency. "We were encouraged to see that AI could stand on its own in making complex predictions. But just as important, this study highlights the vital role of our nurses -- more than 500 participated directly -- demonstrating how human expertise and machine learning can work hand in hand to reimagine care delivery," says co-corresponding senior author Robbie Freeman, DNP, RN, NE-BC3, Chief Digital Transformation Officer at Mount Sinai Health System. "This tool isn't about replacing clinicians; it's about supporting them. By predicting admissions earlier, we can give care teams the time they need to plan, coordinate, and ultimately provide better, more compassionate care. It's inspiring to see AI emerge not as a futuristic idea, but as a practical, real-world solution shaped by the people delivering care every day." The paper is titled "Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System." The study's authors, as listed in the journal, are Jonathan Nover, MBA, RN; Matthew Bai, MD; Prem Tismina; Ganesh Raut; Dhavalkumar Patel; Girish N Nadkarni, MD, MPH; Benjamin S. Abella, MD, MPhil; Eyal Klang, MD, and Robert Freeman, DNP, RN, NE-BC3.

AI Could Help Emergency Rooms Predict Admissions, Driving More Timely, Effective Care | Newswise

Newswise -- New York, NY [August 11, 2025] -- Artificial intelligence (AI) can help emergency department (ED) teams better anticipate which patients will need hospital admission, hours earlier than is currently possible, according to a multi-hospital study by the Mount Sinai Health System. By giving clinicians advance notice, this approach may enhance patient care and the patient experience, reduce overcrowding and "boarding" (when a patient is admitted but remains in the ED because no bed is available), and enable hospitals to direct resources where they're needed most. Among the largest prospective evaluations of AI in the emergency setting to date, the study published in the July 9 online issue of the journal Mayo Clinic Proceedings: Digital Health [https://doi.org/10.1016/j.mcpdig.2025.100249]. In the study, researchers collaborated with more than 500 ED nurses across the seven-hospital Health System. Together, they evaluated a machine learning model trained on data from more than 1 million past patient visits. Over two months, they compared AI-generated predictions with nurses' triage assessments to see whether AI could help identify likely hospital admissions sooner after the patient arrives. "Emergency department overcrowding and boarding have become a national crisis, affecting everything from patient outcomes to financial performance. Industries like airlines and hotels use bookings to forecast demand and plan. In the ED, we don't have reservations. Could you imagine airlines and hotels without reservations, solely forecasting and planning from historical trends? Welcome to health care," says lead author Jonathan Nover, MBA, RN, Vice President of Nursing and Emergency Services, Mount Sinai Health System. "Our goal was to see if AI combined with input from our nurses, could help hasten admission planning, a reservation of sorts. We developed a tool to forecast admissions needs before an order is placed, offering insights that could fundamentally improve how hospitals manage patient flow, leading to better outcomes." The study, involving nearly 50,000 patient visits across Mount Sinai's urban and suburban hospitals, showed that the AI model performed reliably across these diverse hospital settings. Surprisingly, the researchers found that combining human and machine predictions did not significantly boost accuracy, indicating that the AI system alone was a strong predictor. "We wanted to design a model that doesn't just perform well in theory but can actually support decision-making on the front lines of care," says co-corresponding senior author Eyal Klang, MD, Chief of Generative AI in the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai. "By training the algorithm on more than a million patient visits, we aimed to capture meaningful patterns that could help anticipate admissions earlier than traditional methods. The strength of this approach is its ability to turn complex data into timely, actionable insights for clinical teams -- freeing them up to focus less on logistics and more on delivering the personal, compassionate care that only humans can provide." While the study was limited to one health system over a two-month period, the team hopes the findings will serve as a springboard for future live clinical testing. The next phase involves implementing the AI model into real-time workflows and measuring outcomes such as reduced boarding times, improved patient flow, and operational efficiency. "We were encouraged to see that AI could stand on its own in making complex predictions. But just as important, this study highlights the vital role of our nurses -- more than 500 participated directly -- demonstrating how human expertise and machine learning can work hand in hand to reimagine care delivery," says co-corresponding senior author Robbie Freeman, DNP, RN, NE-BC3, Chief Digital Transformation Officer at Mount Sinai Health System. "This tool isn't about replacing clinicians; it's about supporting them. By predicting admissions earlier, we can give care teams the time they need to plan, coordinate, and ultimately provide better, more compassionate care. It's inspiring to see AI emerge not as a futuristic idea, but as a practical, real-world solution shaped by the people delivering care every day." The paper is titled "Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System." The study's authors, as listed in the journal, are Jonathan Nover, MBA, RN; Matthew Bai, MD; Prem Tismina; Ganesh Raut; Dhavalkumar Patel; Girish N Nadkarni, MD, MPH; Benjamin S. Abella, MD, MPhil; Eyal Klang, MD, and Robert Freeman, DNP, RN, NE-BC3. This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. The research was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. -####- About the Mount Sinai Health System Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with 48,000 employees working across seven hospitals, more than 400 outpatient practices, more than 600 research and clinical labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time -- discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients' medical and emotional needs at the center of all treatment. The Health System includes approximately 9,000 primary and specialty care physicians and 10 free-standing joint-venture centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida. Hospitals within the System are consistently ranked by Newsweek's® "The World's Best Smart Hospitals, Best in State Hospitals, World Best Hospitals and Best Specialty Hospitals" and by U.S. News & World Report's® "Best Hospitals" and "Best Children's Hospitals." The Mount Sinai Hospital is on the U.S. News & World Report® "Best Hospitals" Honor Roll for 2025-2026. For more information, visit https://www.mountsinai.org or find Mount Sinai on Facebook, Instagram, LinkedIn, X, and YouTube.

AI Might Be Able To Ease ER Overcrowding And Boarding

By Dennis Thompson HealthDay ReporterTUESDAY, Aug. 12, 2025 (HealthDay News) -- Artificial intelligence (AI) programs can help doctors and nurses predict hours earlier which ER patients will likely require hospital admission, a new study says. An AI program trained on nearly 2 million patient visits became slightly more accurate than ER nurses in predicting which patients would need to be admitted, according to findings published Aug. 11 in the journal Mayo Clinic Proceedings: Digital Health. If this approach proves successful, it could help reduce overcrowding in hospital emergency departments, researchers say. "Emergency department overcrowding and boarding have become a national crisis, affecting everything from patient outcomes to financial performance," said lead researcher Jonathan Nover, vice president of nursing and emergency services at Mount Sinai Health System in New York City. "Industries like airlines and hotels use bookings to forecast demand and plan. In the ED, we don't have reservations," he continued in a news release. "Could you imagine airlines and hotels without reservations, solely forecasting and planning from historical trends? Welcome to health care." Up to 35% of ER patients who require admission wind up spending four or more hours biding their time in spare rooms or busy hallways awaiting a bed, a practice known as "boarding," according to a recent study in the journal Health Affairs. Worse, nearly 5% of patients wait a full day for a bed during the busy winter months, the earlier study found. "Our goal was to see if AI combined with input from our nurses could help hasten admission planning, a reservation of sorts," Nover said. "We developed a tool to forecast admissions needs before an order is placed, offering insights that could fundamentally improve how hospitals manage patient flow, leading to better outcomes." For the project, researchers trained the AI on more than 1.8 million ER visits that had occurred between 2019 and 2023. "By training the algorithm on more than a million patient visits, we aimed to capture meaningful patterns that could help anticipate admissions earlier than traditional methods," co-senior researcher Dr. Eyal Klang, chief of generative AI at the Icahn School of Medicine at Mount Sinai, said in a news release. The team then put the AI up against a cadre of more than 500 ER nurses in evaluating nearly 47,000 patient visits that occurred in September and October 2024 at six emergency departments in the Mount Sinai Health System. The nurses were asked to judge whether a patient would need hospital admission, after performing a quick triage. Researchers also fed the triage results to the AI, to see what it would predict. The nurses proved about 81% accurate in predicting which patients would need hospital admission, compared to 85% accuracy from the AI. "We were encouraged to see that AI could stand on its own in making complex predictions," co-senior researcher Robert Freeman, chief digital transformation officer at Mount Sinai Health System, said in a news release. "But just as important, this study highlights the vital role of our nurses -- more than 500 participated directly -- demonstrating how human expertise and machine learning can work hand in hand to reimagine care delivery." Researchers next plan to implement their AI into real-time workflows and monitor how the program affects boarding times and patient flow through the ER. "This tool isn't about replacing clinicians; it's about supporting them. By predicting admissions earlier, we can give care teams the time they need to plan, coordinate, and ultimately provide better, more compassionate care," Freeman said. "It's inspiring to see AI emerge not as a futuristic idea, but as a practical, real-world solution shaped by the people delivering care every day." More information The American College of Emergency Physicians has more on ER boarding and crowding. SOURCE: Mount Sinai Health System, news release, Aug. 11, 2025