Ingrid Machado Silveira , João Flávio de Freitas Almeida , Luiz Ricardo Pinto , Luiz Antônio Resende Epaminondas , Samuel Vieira Conceição , Elaine Leandro Machado
{"title":"管理流行病爆发和重症监护室病床规划的多阶段优化模型","authors":"Ingrid Machado Silveira , João Flávio de Freitas Almeida , Luiz Ricardo Pinto , Luiz Antônio Resende Epaminondas , Samuel Vieira Conceição , Elaine Leandro Machado","doi":"10.1016/j.health.2024.100342","DOIUrl":null,"url":null,"abstract":"<div><p>Intensive Care Unit (ICU) capacity can be significantly affected by disease outbreaks, epidemics, and pandemics, impeding the operational efficiency of healthcare systems and compromising patient care. This paper presents a multi-stage optimization approach to planning the location and distribution of ICU beds to increase accessibility and reduce mortality caused by a shortage of beds in a geographic region during epidemic events. Using a Brazilian state monthly hospital admissions due to Covid-19 from October 2020 to April 2021, we show the amount and the allocation of extra ICU beds that could reduce mortality, minimize patient travel and transportation, and increase accessibility while considering budget limitations. Our findings show coverage for 21 previously underserved municipalities, providing extra ICU beds for 69 municipalities, ranging from 880 to 1670 beds across seven months. On average, patients are displaced 56% less and access ICUs within 17 ± 2.3 kilometres (CI 95%). The strategy contributes to public health planning and the equitable allocation of hospital resources among the population.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100342"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000443/pdfft?md5=b9329fc322e71a96feb49e6b220e36d5&pid=1-s2.0-S2772442524000443-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A multi-stage optimization model for managing epidemic outbreaks and hospital bed planning in Intensive Care Units\",\"authors\":\"Ingrid Machado Silveira , João Flávio de Freitas Almeida , Luiz Ricardo Pinto , Luiz Antônio Resende Epaminondas , Samuel Vieira Conceição , Elaine Leandro Machado\",\"doi\":\"10.1016/j.health.2024.100342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Intensive Care Unit (ICU) capacity can be significantly affected by disease outbreaks, epidemics, and pandemics, impeding the operational efficiency of healthcare systems and compromising patient care. This paper presents a multi-stage optimization approach to planning the location and distribution of ICU beds to increase accessibility and reduce mortality caused by a shortage of beds in a geographic region during epidemic events. Using a Brazilian state monthly hospital admissions due to Covid-19 from October 2020 to April 2021, we show the amount and the allocation of extra ICU beds that could reduce mortality, minimize patient travel and transportation, and increase accessibility while considering budget limitations. Our findings show coverage for 21 previously underserved municipalities, providing extra ICU beds for 69 municipalities, ranging from 880 to 1670 beds across seven months. On average, patients are displaced 56% less and access ICUs within 17 ± 2.3 kilometres (CI 95%). The strategy contributes to public health planning and the equitable allocation of hospital resources among the population.</p></div>\",\"PeriodicalId\":73222,\"journal\":{\"name\":\"Healthcare analytics (New York, N.Y.)\",\"volume\":\"5 \",\"pages\":\"Article 100342\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2772442524000443/pdfft?md5=b9329fc322e71a96feb49e6b220e36d5&pid=1-s2.0-S2772442524000443-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Healthcare analytics (New York, N.Y.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772442524000443\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Healthcare analytics (New York, N.Y.)","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772442524000443","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multi-stage optimization model for managing epidemic outbreaks and hospital bed planning in Intensive Care Units
Intensive Care Unit (ICU) capacity can be significantly affected by disease outbreaks, epidemics, and pandemics, impeding the operational efficiency of healthcare systems and compromising patient care. This paper presents a multi-stage optimization approach to planning the location and distribution of ICU beds to increase accessibility and reduce mortality caused by a shortage of beds in a geographic region during epidemic events. Using a Brazilian state monthly hospital admissions due to Covid-19 from October 2020 to April 2021, we show the amount and the allocation of extra ICU beds that could reduce mortality, minimize patient travel and transportation, and increase accessibility while considering budget limitations. Our findings show coverage for 21 previously underserved municipalities, providing extra ICU beds for 69 municipalities, ranging from 880 to 1670 beds across seven months. On average, patients are displaced 56% less and access ICUs within 17 ± 2.3 kilometres (CI 95%). The strategy contributes to public health planning and the equitable allocation of hospital resources among the population.