{"id":754,"date":"2020-04-30T11:26:21","date_gmt":"2020-04-30T05:56:21","guid":{"rendered":"http:\/\/www.potentialloops.org\/?p=754"},"modified":"2020-04-30T11:26:23","modified_gmt":"2020-04-30T05:56:23","slug":"model-can-predict-hospital-resilience-for-natural-disasters-pandemics","status":"publish","type":"post","link":"https:\/\/www.potentialloops.org\/?p=754","title":{"rendered":"Model can predict hospital resilience for natural disasters, pandemics"},"content":{"rendered":"\n

Date: April 29, 2020 <\/p>\n\n\n\n

Source 1: Colorado State University <\/p>\n\n\n\n

Source 2: www.sciencedaily.com<\/a><\/p>\n\n\n\n

Summary: Researchers have created a modeling tool that could help cities understand the full functionality and recovery of a healthcare system in the wake of a natural disaster. The model has wider implications for use in pandemics. <\/p>\n\n\n\n

When a natural disaster like an earthquake \nstrikes, a community can literally be shaken to its core. One way to \nassess how well and how quickly that community recovers is to measure \nhow, and how quickly, its hospitals and wider healthcare systems can \nbecome fully functional again and take care of its patients. Predicting \nthe trajectory of that recovery is no easy task.<\/p>\n\n\n\n

That’s because the resilience measures of a healthcare system are \ndizzyingly complex. They span everything from the availability of \nhospital staff, to the protection of critical equipment, to the state of\n the roads for ambulances to travel on, to the efficiency by which \nhospitals can transfer critically ill patients to different hospitals.<\/p>\n\n\n\n

Hussam Mahmoud, an associate professor in the Department of Civil and\n Environmental Engineering at Colorado State University, and his \nstudents spend a lot of time thinking about how to define and describe \n“community resilience.” Mahmoud and graduate student Emad Hassan have \ncreated a modeling tool that could help city planners and emergency \nmanagers understand the full functionality and recovery of a healthcare \nsystem, in the wake of a natural disaster.<\/p>\n\n\n\n

“We set out to develop models allowing us to understand, what is the \ndemand on a hospital healthcare facility after an event like an \nearthquake,” Mahmoud said. “When we started looking into this, we were \nshocked to learn that there are no models currently that allow you to \nunderstand, what is the demand on the hospital, how is the hospital \nbeing impacted by the natural disaster, how is that going to impact \ndemand and capacity, and how will that change over time?”<\/p>\n\n\n\n

Their model, described in a forthcoming issue of the journal Reliability Engineering and System Safety<\/em>,\n has wider implications for use in other disasters, including pandemics,\n like the one the world is experiencing now with COVID-19.<\/p>\n\n\n\n

Healthcare as a complex network<\/strong><\/p>\n\n\n\n

In their paper, Mahmoud and Hassan seek to understand healthcare \nsystems as complex networks that can be visualized as nodes of different\n functionalities. These include number of staffed beds, hospital staff \navailability, housing functionality, patient waiting time for treatment,\n and even things like the probability of patient X going to healthcare \nfacility Y. The availability of water, power, transportation and \ntelecommunication also support hospital operation and factor into the \nmodel. And the researchers define healthcare not just by physical \nmetrics, but also by quality metrics, like the level of customer \nsatisfaction — measured by things like patient wait time.<\/p>\n\n\n\n

To develop and test their framework, the researchers applied it to a \nvirtual community called Centerville, which was developed as a research \ntool by researchers at the CSU Center for Risk-Based Community \nResilience, a National Institute of Standards and Technology Center of \nExcellence of which Mahmoud and Hassan are contributing members. The \nresearchers applied an earthquake scenario to Centerville — an imagined\n mid-sized U.S. community of 50,000 residents with commercial and \nindustrial zones, schools, fire stations and hospitals — to see how it \nwould fare. Using the virtual environment helped them highlight the \ncapabilities of their model and the impact of decisions made as the \ncommunity recovered.<\/p>\n\n\n\n

Pandemic applications<\/strong><\/p>\n\n\n\n

The purpose of Mahmoud and Hassan’s work is to define the parameters needed to be measured by communities to assess how prepared they are for natural disasters. In the wake of COVID-19, Mahmoud said, they have begun using their model to theorize and predict how hospital networks can better manage pandemics by identifying gaps in resources and potential bottlenecks according to different worst-case scenarios. They are now working with the National Center for Disaster Medicine and Public Health to further refine the model and apply it to pandemic planning.<\/p>\n\n\n\n

Story Source:<\/strong><\/p>\n\n\n\n

Materials<\/a> provided by Colorado State University<\/strong><\/a>. Original written by Anne Manning. Note: Content may be edited for style and length.<\/em><\/p>\n\n\n\n

www.sciencedaily.com<\/a><\/p>\n\n\n\n

\n\n\n\n

Journal Reference<\/strong>:<\/p>\n\n\n\n

  1. Emad M. Hassan, Hussam Mahmoud. An Integrated Socio-Technical Approach for Post-Earthquake Recovery of Interdependent Healthcare System<\/strong>. Reliability Engineering & System Safety<\/em>, 2020; 106953 DOI: 10.1016\/j.ress.2020.106953<\/a>\n<\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"

    Date: April 29, 2020 Source 1: Colorado State University Source 2: www.sciencedaily.com Summary: Researchers have created a modeling tool that could help cities understand the full functionality and recovery of a healthcare system in the wake of a natural disaster. The model has wider implications for use in pandemics. When a natural disaster like an […]<\/p>\n","protected":false},"author":1,"featured_media":685,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[35],"tags":[36,71,70,69,72,40,41],"class_list":["post-754","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-coronavirus","tag-corona","tag-hospital","tag-natural-disasters","tag-pandemics","tag-predict","tag-research","tag-writing"],"aioseo_notices":[],"yoast_head":"\nModel can predict hospital resilience for natural disasters, pandemics -<\/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:\/\/www.potentialloops.org\/?p=754\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Model can predict hospital resilience for natural disasters, pandemics -\" \/>\n<meta property=\"og:description\" content=\"Date: April 29, 2020 Source 1: Colorado State University Source 2: www.sciencedaily.com Summary: Researchers have created a modeling tool that could help cities understand the full functionality and recovery of a healthcare system in the wake of a natural disaster. 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