Tahir Ekin , Ozan Kocadagli , NathanielD. Bastian , LawrenceV. Fulton , Paul M. Griffin
{"title":"Fuzzy decision making in health systems: a resource allocation model","authors":"Tahir Ekin , Ozan Kocadagli , NathanielD. Bastian , LawrenceV. Fulton , Paul M. Griffin","doi":"10.1007/s40070-015-0049-x","DOIUrl":null,"url":null,"abstract":"<div><p>The efficient use of resources in health systems is important due to the increasing demand and limited funding. Large health systems often have fixed input resources (such as budget and staffing) to be allocated among individual hospitals/clinics with particular target output levels. We propose an optimization model with fuzzy constraints that can be used for automatic resource re-allocation with respect to different levels of risk preferences. We illustrate its applicability using data from a U.S. Army hospital network. The implications of the proposed fuzzy decision-making model for healthcare decision makers and its relevance to healthcare policy and management are also discussed.</p></div>","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"4 3","pages":"Pages 245-267"},"PeriodicalIF":2.3000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40070-015-0049-x","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURO Journal on Decision Processes","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2193943821000674","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 9
Abstract
The efficient use of resources in health systems is important due to the increasing demand and limited funding. Large health systems often have fixed input resources (such as budget and staffing) to be allocated among individual hospitals/clinics with particular target output levels. We propose an optimization model with fuzzy constraints that can be used for automatic resource re-allocation with respect to different levels of risk preferences. We illustrate its applicability using data from a U.S. Army hospital network. The implications of the proposed fuzzy decision-making model for healthcare decision makers and its relevance to healthcare policy and management are also discussed.