{"version":"1.0","provider_name":"RigenerAI | AI per la sanit\u00e0","provider_url":"https:\/\/rigenerai.com\/en\/","author_name":"debora","author_url":"https:\/\/rigenerai.com\/en\/author\/debora\/","title":"Data-driven demand reallocation in facility networks - RigenerAI | AI per la sanit\u00e0","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"EqXZ90NI6f\"><a href=\"https:\/\/rigenerai.com\/en\/case-studies\/data-driven-demand-reallocation-in-facility-networks\/\">Data-driven demand reallocation in facility networks<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/rigenerai.com\/en\/case-studies\/data-driven-demand-reallocation-in-facility-networks\/embed\/#?secret=EqXZ90NI6f\" width=\"600\" height=\"338\" title=\"&#8220;Data-driven demand reallocation in facility networks&#8221; &#8212; RigenerAI | AI per la sanit\u00e0\" data-secret=\"EqXZ90NI6f\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/rigenerai.com\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","thumbnail_url":"https:\/\/rigenerai.com\/wp-content\/uploads\/2026\/03\/Riallocazione-data-driven-domanda-in-network-di-strutture_LR.jpg","thumbnail_width":2000,"thumbnail_height":1120,"description":"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."}