{"id":28249,"date":"2025-08-12T13:55:39","date_gmt":"2025-08-12T13:55:39","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=28249"},"modified":"2025-08-12T13:55:41","modified_gmt":"2025-08-12T13:55:41","slug":"ai-could-help-emergency-room-teams-predict-admissions-boosting-patient-care","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/ai-could-help-emergency-room-teams-predict-admissions-boosting-patient-care\/","title":{"rendered":"AI Could Help Emergency Room Teams Predict Admissions, Boosting Patient Care"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">A ground-breaking multi-hospital study by Mount Sinai Health System reveals that AI can predict hospital admissions hours in advance, promising improved patient care and efficiency in emergency departments.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-0dfbf163 wp-block-group-is-layout-flex\"><div style=\"font-size:16px;\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p>A recent study conducted by the Mount Sinai Health System reveals that artificial intelligence can help emergency department (ED) teams better anticipate which patients will need hospital admission. The AI model achieved this feat hours earlier than current methods, significantly improving patient care and reducing overcrowding and &#8220;boarding,&#8221; a scenario where admitted patients remain in the ED due to a lack of available beds.<\/p>\n\n\n\n<p>This pioneering research, <a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2949761225000562\" target=\"_blank\" rel=\"noopener\" title=\"\">published<\/a> in the journal Mayo Clinic Proceedings: Digital Health, marks one of the most extensive evaluations of AI in emergency settings to date. <\/p>\n\n\n\n<p>The study involved over 500 ED nurses across seven hospitals and analyzed data from more than 1 million patient visits to train a machine learning model.<\/p>\n\n\n\n<p>&#8220;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\u2019t have reservations. Could you imagine airlines and hotels without reservations, solely forecasting and planning from historical trends? Welcome to health care,&#8221; lead author Jonathan Nover, a vice president of nursing and emergency services at Mount Sinai Health System, said in a news release. <\/p>\n\n\n\n<p>&#8220;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,\u201d he added.<\/p>\n\n\n\n<p>The research assessed nearly 50,000 patient visits across Mount Sinai\u2019s urban and suburban hospitals, showing that the AI model performed consistently well in these varied settings. Surprisingly, the combination of human and machine predictions did not significantly improve accuracy, indicating the AI system alone was a strong predictor.<\/p>\n\n\n\n<p>&#8220;We wanted to design a model that doesn\u2019t just perform well in theory but can actually support decision-making on the front lines of care,&#8221; added co-corresponding senior author Eyal Klang, chief of generative AI in the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai. &#8220;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 \u2014 freeing them up to focus less on logistics and more on delivering the personal, compassionate care that only humans can provide.\u201d<\/p>\n\n\n\n<p>While the pilot study was restricted to a two-month period and within one health system, the team hopes these findings will spark further live clinical testing. <\/p>\n\n\n\n<p>The next phase involves integrating the AI model into real-time workflows, monitoring outcomes such as reduced boarding times, improved patient flow and operational efficiency.<\/p>\n\n\n\n<p>&#8220;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 \u2014 more than 500 participated directly \u2014 demonstrating how human expertise and machine learning can work hand in hand to reimagine care delivery,\u201d added co-corresponding senior author Robbie Freeman, chief digital transformation officer at Mount Sinai Health System. &#8220;This tool isn\u2019t about replacing clinicians; it\u2019s 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\u2019s 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.\u201d<\/p>\n\n\n\n<div style=\"height:11px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>Source:<\/strong> <a href=\"https:\/\/www.mountsinai.org\/about\/newsroom\/2025\/ai-could-help-emergency-rooms-predict-admissions-driving-more-timely-effective-care\" target=\"_blank\" rel=\"noopener\" title=\"\">Mount Sinai<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A recent study conducted by the Mount Sinai Health System reveals that artificial intelligence can help emergency department (ED) teams better anticipate which patients will need hospital admission. The AI model achieved this feat hours earlier than current methods, significantly improving patient care and reducing overcrowding and &#8220;boarding,&#8221; a scenario where admitted patients remain in [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[8,27],"tags":[172],"class_list":["post-28249","post","type-post","status-publish","format-standard","hentry","category-ai","category-health-care","tag-icahn-school-of-medicine-at-mount-sinai"],"acf":[],"aioseo_notices":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"The University Network","author_link":"https:\/\/www.tun.com\/home\/author\/funky_junkie\/"},"uagb_comment_info":0,"uagb_excerpt":"A recent study conducted by the Mount Sinai Health System reveals that artificial intelligence can help emergency department (ED) teams better anticipate which patients will need hospital admission. The AI model achieved this feat hours earlier than current methods, significantly improving patient care and reducing overcrowding and &#8220;boarding,&#8221; a scenario where admitted patients remain in&hellip;","_links":{"self":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/28249","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/comments?post=28249"}],"version-history":[{"count":8,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/28249\/revisions"}],"predecessor-version":[{"id":28298,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/28249\/revisions\/28298"}],"wp:attachment":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/media?parent=28249"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/categories?post=28249"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/tags?post=28249"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}