{"title":"床短缺的分析:连贯的度量、预测和优化","authors":"Jingui Xie, G. Loke, Melvyn Sim, S. Lam","doi":"10.1287/opre.2021.2231","DOIUrl":null,"url":null,"abstract":"This study proposes a risk-adjusted version of bed occupancy rates (BORs) that can be used for patient admission control and bed capacity planning in hospitals. Simulations indicate a potential increase of 11% in elective patient admissions by planning using the proposed bed shortage index (BSI) as opposed to the traditional BOR. The BSI is also illustrated for purposes of bed capacity allocation between departments and across acute and nonacute hospitals.","PeriodicalId":19546,"journal":{"name":"Oper. Res.","volume":"1 1","pages":"23-46"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The Analytics of Bed Shortages: Coherent Metric, Prediction, and Optimization\",\"authors\":\"Jingui Xie, G. Loke, Melvyn Sim, S. Lam\",\"doi\":\"10.1287/opre.2021.2231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study proposes a risk-adjusted version of bed occupancy rates (BORs) that can be used for patient admission control and bed capacity planning in hospitals. Simulations indicate a potential increase of 11% in elective patient admissions by planning using the proposed bed shortage index (BSI) as opposed to the traditional BOR. The BSI is also illustrated for purposes of bed capacity allocation between departments and across acute and nonacute hospitals.\",\"PeriodicalId\":19546,\"journal\":{\"name\":\"Oper. Res.\",\"volume\":\"1 1\",\"pages\":\"23-46\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oper. Res.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1287/opre.2021.2231\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oper. Res.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1287/opre.2021.2231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Analytics of Bed Shortages: Coherent Metric, Prediction, and Optimization
This study proposes a risk-adjusted version of bed occupancy rates (BORs) that can be used for patient admission control and bed capacity planning in hospitals. Simulations indicate a potential increase of 11% in elective patient admissions by planning using the proposed bed shortage index (BSI) as opposed to the traditional BOR. The BSI is also illustrated for purposes of bed capacity allocation between departments and across acute and nonacute hospitals.