{"id":2793,"date":"2026-03-14T21:40:43","date_gmt":"2026-03-14T21:40:43","guid":{"rendered":"https:\/\/rigenerai.com\/ai-for-health-care\/data-driven-demand-reallocation-in-facility-networks\/"},"modified":"2026-03-17T21:01:02","modified_gmt":"2026-03-17T21:01:02","slug":"data-driven-demand-reallocation-in-facility-networks","status":"publish","type":"post","link":"https:\/\/rigenerai.com\/en\/case-studies\/data-driven-demand-reallocation-in-facility-networks\/","title":{"rendered":"Data-driven demand reallocation in facility networks"},"content":{"rendered":"\n<p>When service demand is variable and resources are limited, the risk of congestion grows rapidly: waiting times increase, intake capacity is reduced, and cascading inefficiencies are generated. The COVID-19 pandemic has amplified these dynamics, making clear the need for more integrated and coordinated solutions to manage peak influx and overflow of patients.<\/p>\n\n<p>In this context, we proposed a centralized patient reallocation strategy focused on non-urgent cases. The idea is to take advantage of demand-capacity imbalances between facilities: when a hospital is saturated, some of the load can be redirected to less congested facilities, improving the distribution of patients throughout the system and reducing overall congestion.<\/p>\n\n<p>The implementation was developed in a network of 4 emergency rooms affiliated with a single ASST that have an annual number of accesses exceeding 100,000.<\/p>\n\n<h2 class=\"wp-block-heading\">Methodological strategy<\/h2>\n\n<p><strong>Step 1 &#8211; Stochastic Optimization Model<\/strong><br\/>Development of a stochastic model to optimize the reallocation of nonurgent patients under uncertainty scenarios, with objectives of:<\/p>\n\n<ul class=\"wp-block-list\">\n<li>Minimize the overall time in the system (waiting + handling)<\/li>\n\n\n\n<li>Balancing demand and capacity between hospitals\/clinics<\/li>\n<\/ul>\n\n<p><strong>Step 2 &#8211; Pooling (patient grouping) and fairness criteria<\/strong><br\/>Introduction of patient pooling strategies and fairness constraints to ensure reallocation:<\/p>\n\n<ul class=\"wp-block-list\">\n<li>flexible (adaptable to peaks)<\/li>\n\n\n\n<li>fair (avoiding making patients wait beyond reasonable time intervals),<\/li>\n<\/ul>\n\n<h2 class=\"wp-block-heading\">Results<\/h2>\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"661\" src=\"https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/riallocazione-data-driven-della-domanda-in-network-di-strutture_LR-1024x661.jpg\" alt=\"\" class=\"wp-image-2430\" srcset=\"https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/riallocazione-data-driven-della-domanda-in-network-di-strutture_LR-1024x661.jpg 1024w, https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/riallocazione-data-driven-della-domanda-in-network-di-strutture_LR-300x194.jpg 300w, https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/riallocazione-data-driven-della-domanda-in-network-di-strutture_LR-768x496.jpg 768w, https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/riallocazione-data-driven-della-domanda-in-network-di-strutture_LR.jpg 1123w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n<p><strong>Better flow management<\/strong><br\/>The on-demand approach <strong>reduces wait times by 25-30%<\/strong>, accelerating access to care and increasing throughput.<\/p>\n\n<p><strong>Optimizing &#8220;fleet&#8221;\/transfers<\/strong><br\/>Pooling strategy increases the number of reallocated patients on average, <strong>reducing fleet costs by 5-10%<\/strong>.<\/p>\n\n<p><strong>Greater stability in resource planning<\/strong><br\/>Consistent routing strategy <strong>reduces waiting time by about 10 percent<\/strong>, making resource allocation more predictable and facilitating optimal fleet sizing.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In health care systems with variable demand and limited resources, congestion in emergency rooms can grow rapidly, increasing wait times and operational inefficiencies. Strategies for centrally reallocating nonurgent patients among facilities with different levels of saturation can be adopted to deal with these peak influxes.<\/p>\n<p>By applying optimization models and equity criteria in an emergency room network, the approach allows for better balancing of demand and capacity, significantly reducing waiting times, and improving the overall management of patient flows.<\/p>\n","protected":false},"author":1,"featured_media":2966,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34,35],"tags":[],"class_list":["post-2793","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>Data-driven demand reallocation in facility networks - 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\/data-driven-demand-reallocation-in-facility-networks\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data-driven demand reallocation in facility networks - RigenerAI | AI per la sanit\u00e0\" \/>\n<meta property=\"og:description\" content=\"In health care systems with variable demand and limited resources, congestion in emergency rooms can grow rapidly, increasing wait times and operational inefficiencies. Strategies for centrally reallocating nonurgent patients among facilities with different levels of saturation can be adopted to deal with these peak influxes. By applying optimization models and equity criteria in an emergency room network, the approach allows for better balancing of demand and capacity, significantly reducing waiting times, and improving the overall management of patient flows.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/rigenerai.com\/en\/case-studies\/data-driven-demand-reallocation-in-facility-networks\/\" \/>\n<meta property=\"og:site_name\" content=\"RigenerAI | AI per la sanit\u00e0\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-14T21:40:43+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-17T21:01:02+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/Riallocazione-data-driven-domanda-in-network-di-strutture_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=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/data-driven-demand-reallocation-in-facility-networks\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/data-driven-demand-reallocation-in-facility-networks\\\/\"},\"author\":{\"name\":\"debora\",\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/#\\\/schema\\\/person\\\/4a84c84bbeefc1ddf7c4ca095fef2fc4\"},\"headline\":\"Data-driven demand reallocation in facility networks\",\"datePublished\":\"2026-03-14T21:40:43+00:00\",\"dateModified\":\"2026-03-17T21:01:02+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/data-driven-demand-reallocation-in-facility-networks\\\/\"},\"wordCount\":268,\"publisher\":{\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/data-driven-demand-reallocation-in-facility-networks\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/rigenerai.com\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/Riallocazione-data-driven-domanda-in-network-di-strutture_LR.jpg\",\"articleSection\":[\"AI for health care\",\"Case studies\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/data-driven-demand-reallocation-in-facility-networks\\\/\",\"url\":\"https:\\\/\\\/rigenerai.com\\\/en\\\/case-studies\\\/data-driven-demand-reallocation-in-facility-networks\\\/\",\"name\":\"Data-driven demand reallocation in facility networks - 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