In a groundbreaking study poised to redefine emergency medicine logistics, researchers at the Mount Sinai Health System have unveiled an artificial intelligence (AI) model capable of predicting hospital admissions from the emergency department (ED) significantly earlier than conventional methods. This advance represents a critical leap forward in reducing overcrowding and patient boarding times, challenges that plague emergency care nationwide.
Emergency departments across the United States frequently grapple with the problem of “boarding,” a phenomenon wherein admitted patients remain in the ED for extended periods due to unavailable inpatient beds. This bottleneck leads to diminished patient outcomes, increased staff burnout, and serious operational inefficiencies. Unlike industries such as airlines and hospitality that rely on upfront bookings and reservations to forecast demand, hospitals historically lack such predictive foresight. Mount Sinai’s new AI-driven approach aims to change this paradigm by functioning as a predictive “reservation system,” offering admissions forecasts well before formal orders are placed.
The AI model was trained using a vast dataset of over one million historical patient visits, encompassing demographics, clinical data, presenting complaints, vital signs, and initial nursing triage assessments. This large-scale machine learning endeavor employed advanced computational simulation techniques to unearth complex, non-linear patterns often imperceptible to the human eye. By learning from this rich continuum of prior cases, the algorithm can identify subtle yet clinically significant signals that foreshadow which patients will require hospital admission.
To rigorously evaluate the tool’s real-world potential, the research team collaborated with more than 500 emergency nurses spanning seven hospitals within Mount Sinai’s system, representing both urban and suburban settings. Over a two-month prospective period involving nearly 50,000 patient encounters, the AI’s predictive outputs were compared against frontline nurses’ triage judgments. Remarkably, the model demonstrated a high degree of accuracy in anticipating admissions several hours earlier than traditional assessments.
One of the study’s most striking findings is that the AI system alone rivaled the predictive capability of seasoned nurses, and the integration of both human and machine predictions did not produce a statistically significant improvement in overall accuracy. This underscores the model’s robustness and reliability as a standalone decision support tool, providing insights that could free clinical staff from some of the cognitive burden associated with operational planning.
Jonathan Nover, MBA, RN, Vice President of Nursing and Emergency Services at Mount Sinai, emphasized this transformational potential, likening current ED workflows to industries lacking reservation systems. He noted, “Emergency department overcrowding and boarding have become a national crisis, affecting everything from patient outcomes to financial performance. Our AI tool offers a new way to forecast admissions needs hours ahead, providing a kind of reservation that helps better allocate resources and improve patient flow.”
Eyal Klang, MD, Chief of Generative AI in the Windreich Department of Artificial Intelligence and Human Health, elaborated on the technical foundations of the algorithm. He mentioned that the model harnesses generative AI techniques, which allow it to synthesize diverse clinical features and temporal data sequences. The approach translates multifaceted patient data into actionable, real-time insights that frontline teams can deploy to optimize care delivery — all while preserving the irreplaceable human elements of clinical judgment and compassionate care.
Despite the study’s promising results, the research team emphasizes that this work represents an early but critical step towards fully integrated AI-driven workflows. Planned next phases will involve embedding the model within live clinical environments to measure its impact on key performance indicators, including reductions in boarding times, enhanced patient throughput, and improved operational efficiency.
Furthermore, the AI system’s capacity to adapt across heterogeneous hospital environments attests to the model’s generalizability. It performed consistently across Mount Sinai’s diverse hospital network, which spans demographics, acuity levels, and patient volumes. This versatility hints at broader applicability for health systems facing similar pressures worldwide.
Critically, the research underscores the complementary relationship between human expertise and machine learning. While AI offers powerful predictive capabilities, clinical teams remain essential for interpreting nuanced cases and providing personalized care. Robbie Freeman, DNP, RN, NE-BC3, Chief Digital Transformation Officer at Mount Sinai, stated, “This tool isn’t about replacing clinicians; it’s about supporting them. By predicting admissions earlier, we empower care teams to plan and coordinate, ultimately delivering better, more compassionate care.”
The study was published on July 9, 2025, in the peer-reviewed journal Mayo Clinic Proceedings: Digital Health. It stands among the largest prospective evaluations of AI in emergency settings to date, representing a fusion of computational innovation, large-scale clinical collaboration, and a shared mission to tackle systemic challenges in patient care.
Funded in part by grants from the National Institutes of Health and supported by Mount Sinai’s Scientific Computing and Data resources, this research exemplifies how interdisciplinary collaboration can push the boundaries of healthcare technology. The multidisciplinary author team includes clinicians, data scientists, and nursing leaders working in concert to translate machine learning advancements into tangible clinical benefits.
As AI continues to permeate healthcare, this study shines a light on practical applications that go beyond theoretical promise. Its success signals that, with rigorous development and thoughtful integration, intelligent systems can become indispensable allies for overburdened emergency departments—turning chaos into coordination, and uncertainty into foresight, all while maintaining the human touch at the heart of healing.
Subject of Research: People
Article Title: Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System
News Publication Date: 9-Jul-2025
Web References:
https://doi.org/10.1016/j.mcpdig.2025.100249
References:
Nover J, Bai M, Tismina P, Raut G, Patel D, Nadkarni GN, Abella BS, Klang E, Freeman R. Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System. Mayo Clinic Proceedings: Digital Health. 2025 Jul 9.
Keywords: Emergency rooms, artificial intelligence, hospital admissions, emergency department overcrowding, machine learning, clinical decision support, patient flow management
