Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unit

Detalhes bibliográficos
Autor(a) principal: Cotrim, Carolina De Jesus Simões
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/154004
Resumo: Emergency Department (ED) overcrowding has been negatively affecting both public and private hospitals all around the globe. A more efficient planning of ED resources can help to mitigate this phenomenon. Thus, a Machine Learning multi-step-ahead predictive tool was developed to forecast weekly ED arrivals. Hence, ED managers can make decisions based on these predictions, allowing for a smooth ED functioning where the resources provided match current ED demand. First, the predictive tool was used to forecast ED demand in a bigger unit of a private Portuguese healthcare provider, CUF, and then, the same tool was used in a smaller unit.
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spelling Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unitHealthcareEmergency departmentMachine learningTime seriesMulti-step-ahead forecastingDomínio/Área Científica::Ciências Sociais::Economia e GestãoEmergency Department (ED) overcrowding has been negatively affecting both public and private hospitals all around the globe. A more efficient planning of ED resources can help to mitigate this phenomenon. Thus, a Machine Learning multi-step-ahead predictive tool was developed to forecast weekly ED arrivals. Hence, ED managers can make decisions based on these predictions, allowing for a smooth ED functioning where the resources provided match current ED demand. First, the predictive tool was used to forecast ED demand in a bigger unit of a private Portuguese healthcare provider, CUF, and then, the same tool was used in a smaller unit.Velho, IolandaRUNCotrim, Carolina De Jesus Simões2023-06-16T10:50:01Z2023-01-092023-01-092023-01-09T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/154004TID:203310560enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:36:30Zoai:run.unl.pt:10362/154004Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:28.045907Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unit
title Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unit
spellingShingle Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unit
Cotrim, Carolina De Jesus Simões
Healthcare
Emergency department
Machine learning
Time series
Multi-step-ahead forecasting
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unit
title_full Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unit
title_fullStr Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unit
title_full_unstemmed Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unit
title_sort Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a small-sized unit
author Cotrim, Carolina De Jesus Simões
author_facet Cotrim, Carolina De Jesus Simões
author_role author
dc.contributor.none.fl_str_mv Velho, Iolanda
RUN
dc.contributor.author.fl_str_mv Cotrim, Carolina De Jesus Simões
dc.subject.por.fl_str_mv Healthcare
Emergency department
Machine learning
Time series
Multi-step-ahead forecasting
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Healthcare
Emergency department
Machine learning
Time series
Multi-step-ahead forecasting
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description Emergency Department (ED) overcrowding has been negatively affecting both public and private hospitals all around the globe. A more efficient planning of ED resources can help to mitigate this phenomenon. Thus, a Machine Learning multi-step-ahead predictive tool was developed to forecast weekly ED arrivals. Hence, ED managers can make decisions based on these predictions, allowing for a smooth ED functioning where the resources provided match current ED demand. First, the predictive tool was used to forecast ED demand in a bigger unit of a private Portuguese healthcare provider, CUF, and then, the same tool was used in a smaller unit.
publishDate 2023
dc.date.none.fl_str_mv 2023-06-16T10:50:01Z
2023-01-09
2023-01-09
2023-01-09T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/154004
TID:203310560
url http://hdl.handle.net/10362/154004
identifier_str_mv TID:203310560
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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