{"id":2779,"date":"2026-03-14T22:01:50","date_gmt":"2026-03-14T22:01:50","guid":{"rendered":"https:\/\/rigenerai.com\/ai-sanita\/prioritization-in-the-emergency-room-with-llm\/"},"modified":"2026-03-17T20:52:52","modified_gmt":"2026-03-17T20:52:52","slug":"prioritization-in-the-emergency-room-with-llm","status":"publish","type":"post","link":"https:\/\/rigenerai.com\/en\/case-studies\/prioritization-in-the-emergency-room-with-llm\/","title":{"rendered":"Prioritization in the emergency room with LLM"},"content":{"rendered":"\n<p>In emergency rooms, triage identifies immediate-risk patients well, but reduces strong clinical heterogeneity in a few classes of urgency. Within the same class, very different patients (by acute physiological severity or clinical\/management complexity) are often managed as if they were operationally equivalent.<\/p>\n\n<p>This poor structuring of intra-class priority results in:<\/p>\n\n<ul class=\"wp-block-list\">\n<li>high decision-making discretion<\/li>\n\n\n\n<li>variability between operators<\/li>\n\n\n\n<li>Inefficiencies in flows especially in congested conditions and increased waiting times for the most vulnerable patients<\/li>\n<\/ul>\n\n<p>In addition, much relevant information is contained in unstructured texts (brief history, nursing notes) and is difficult to use with traditional tools. Thus, decision support is needed that structures and enhances the information already available at triage, improving operational decisions without replacing clinical judgment. The implementation was developed in a medium to large emergency department with daily accesses close to 150 patients.<\/p>\n\n<h2 class=\"wp-block-heading\">Methodological approach<\/h2>\n\n<p><strong>Step 1: LLM &#8220;domain-adapted&#8221; pipeline with clinical validation<\/strong><\/p>\n\n<ul class=\"wp-block-list\">\n<li>Integration of structured data (vital parameters, age, gender) and triage texts.<\/li>\n\n\n\n<li>Normalization of clinical text (corrections, acronym expansion, semantic standardization).<\/li>\n\n\n\n<li>LLM inference calibrated with prompts designed and validated with PS physicians.<\/li>\n\n\n\n<li>Output: two ordinal assessments per patient:\n<ul class=\"wp-block-list\">\n<li>Intensity (acute physiological severity)<\/li>\n\n\n\n<li>Complexity (vulnerability and management burden<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n<p>If triage remains unchanged: the model adds a second level of decision-making.<\/p>\n\n<p><strong>Step 2: &#8220;Agentic&#8221; prioritization algorithm + operational simulation<\/strong><\/p>\n\n<ul class=\"wp-block-list\">\n<li>LLM measures feed into an algorithm that proposes intra-class prioritization via composite index (weighting between intensity and complexity).<\/li>\n\n\n\n<li>Evaluation by discrete-event simulation, isolating the effect of the prioritization rule alone at constant demand\/capacity.<\/li>\n\n\n\n<li>The system acts as real-time decision support: it produces interpretable operational signals and improves performance without changing clinical protocols.<\/li>\n<\/ul>\n\n<h2 class=\"wp-block-heading\">Results<\/h2>\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"495\" height=\"401\" data-id=\"2436\" src=\"https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/Supporto-decisionale-PS.png\" alt=\"\" class=\"wp-image-2436\" srcset=\"https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/Supporto-decisionale-PS.png 495w, https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/Supporto-decisionale-PS-300x243.png 300w\" sizes=\"(max-width: 495px) 100vw, 495px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"529\" height=\"468\" data-id=\"2435\" src=\"https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/CDF-tempo-servizio-PS.png\" alt=\"\" class=\"wp-image-2435\" srcset=\"https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/CDF-tempo-servizio-PS.png 529w, https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/CDF-tempo-servizio-PS-300x265.png 300w\" sizes=\"(max-width: 529px) 100vw, 529px\" \/><\/figure>\n<\/figure>\n\n<p><strong>Alignment with clinical judgment<\/strong><br\/>The model&#8217;s assessments are consistent with independent medical assessments, particularly on the complexity dimension.<\/p>\n\n<p><strong>Reduced waiting time in congestion<\/strong><br\/>Secondary prioritization based on intensity+complexity reduces the average waiting time in the analyzed class by about 19-29 minutes per patient, with indirect benefits on other classes as well.<\/p>\n\n<p><strong>Improving overall flow<\/strong><br\/>A more structured redistribution of waits reduces systemic congestion by targeting the most delay-sensitive patients and limiting service time inflation.<\/p>\n\n<p><strong>More transparent and governable decisions<\/strong><br\/>Makes explicit what are now informal logics, improving transparency, reproducibility and governance of the process.<\/p>\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In emergency rooms, triage correctly classifies clinical urgency, but within the same class remain very different patients in terms of severity and care complexity. This heterogeneity can generate decision-making discretion, variability among operators, and inefficiencies in flows, especially during congestion phases.<\/p>\n<p>By integrating structured data and information contained in triage texts through artificial intelligence models and prioritization algorithms, real-time operational decisions can be supported. The approach allows for improved management of intraclass priorities, reduced wait times, and more transparent and governable decision making, without replacing clinical judgment.<\/p>\n","protected":false},"author":1,"featured_media":2948,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34,35],"tags":[],"class_list":["post-2779","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-for-health-care","category-case-studies"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Prioritization in the emergency room with LLM - RigenerAI | AI per la sanit\u00e0<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/rigenerai.com\/en\/case-studies\/prioritization-in-the-emergency-room-with-llm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Prioritization in the emergency room with LLM - RigenerAI | AI per la sanit\u00e0\" \/>\n<meta property=\"og:description\" content=\"In emergency rooms, triage correctly classifies clinical urgency, but within the same class remain very different patients in terms of severity and care complexity. This heterogeneity can generate decision-making discretion, variability among operators, and inefficiencies in flows, especially during congestion phases. By integrating structured data and information contained in triage texts through artificial intelligence models and prioritization algorithms, real-time operational decisions can be supported. The approach allows for improved management of intraclass priorities, reduced wait times, and more transparent and governable decision making, without replacing clinical judgment.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/rigenerai.com\/en\/case-studies\/prioritization-in-the-emergency-room-with-llm\/\" \/>\n<meta property=\"og:site_name\" content=\"RigenerAI | AI per la sanit\u00e0\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-14T22:01:50+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-17T20:52:52+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/Prioritizzazione-in-Pronto-Soccorso-con-LLM_LR.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2000\" \/>\n\t<meta property=\"og:image:height\" content=\"1120\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"debora\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"debora\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/prioritization-in-the-emergency-room-with-llm\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/prioritization-in-the-emergency-room-with-llm\\\/\"},\"author\":{\"name\":\"debora\",\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/#\\\/schema\\\/person\\\/4a84c84bbeefc1ddf7c4ca095fef2fc4\"},\"headline\":\"Prioritization in the emergency room with LLM\",\"datePublished\":\"2026-03-14T22:01:50+00:00\",\"dateModified\":\"2026-03-17T20:52:52+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/prioritization-in-the-emergency-room-with-llm\\\/\"},\"wordCount\":364,\"publisher\":{\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/prioritization-in-the-emergency-room-with-llm\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/rigenerai.com\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/Prioritizzazione-in-Pronto-Soccorso-con-LLM_LR.jpg\",\"articleSection\":[\"AI for health care\",\"Case studies\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/prioritization-in-the-emergency-room-with-llm\\\/\",\"url\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/prioritization-in-the-emergency-room-with-llm\\\/\",\"name\":\"Prioritization in the emergency room with LLM - 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