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.
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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 11 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.
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