Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/8122 |
Resumo: | The scarce resources of public health makes the administrator manage the destination of resources, aiming to rationalize and optimize its collection, in order to improve the assistance to patients because the hospital is a public institution and does not get profits but promotes the community well-being. Thus, the hospital infection is acquired after the patient comes to the hospital of after he goes home and might be associated with his staying in hospital or with hospital procedures. This cost must be avoided. Once the complete eradication is not impossible, it is necessary to analyze and to control the monthly cost of the main antibiotics used for its treatment so that there is enough knowledge to foresee the resource collection to buy them. In this context, the main reason of this research is to carry out a forecast of the monthly cost and of the resource collection needed to purchase those medicine used in the treatment of hospital infections at the University Hospital of Santa Maria. To do so, a methodology for forecast by dynamic and multiple linear regressions was used. They were combined with to a multivariate technique by principal components. The technique of principal components was used to eliminate the multiple linearity existing among the original variants so, the resulting principal components were used as variables in the construction of the model of multiple linear regression and of dynamic regression. Therefore, these methodologies are applied to a case study of public health, in order to foresee and to conclude about which model is more suitable to forecast the monthly cost of antibiotics in hospital infections. The results obtained from the two models were considered satisfactory but the model of dynamic regression was chosen to be more suitable because it presented a mean absolute percentage error (MAPE). Finally, the findings might be a managerial tool for hospital administration when they offer subsides for the budget of planning and of the resource finances, especially in a time when resources are globally scarce, making health even more expensive. |
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Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa MariaResource collection for anti-microbial at the University Hospital of Santa Maria by means of forecastsRegressão linear múltiplaRegressão dinâmicaAnálise de componentes principaisCustos em saúdeMultiple linear regressionDynamic regressionPrincipal components analysisCNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAOThe scarce resources of public health makes the administrator manage the destination of resources, aiming to rationalize and optimize its collection, in order to improve the assistance to patients because the hospital is a public institution and does not get profits but promotes the community well-being. Thus, the hospital infection is acquired after the patient comes to the hospital of after he goes home and might be associated with his staying in hospital or with hospital procedures. This cost must be avoided. Once the complete eradication is not impossible, it is necessary to analyze and to control the monthly cost of the main antibiotics used for its treatment so that there is enough knowledge to foresee the resource collection to buy them. In this context, the main reason of this research is to carry out a forecast of the monthly cost and of the resource collection needed to purchase those medicine used in the treatment of hospital infections at the University Hospital of Santa Maria. To do so, a methodology for forecast by dynamic and multiple linear regressions was used. They were combined with to a multivariate technique by principal components. The technique of principal components was used to eliminate the multiple linearity existing among the original variants so, the resulting principal components were used as variables in the construction of the model of multiple linear regression and of dynamic regression. Therefore, these methodologies are applied to a case study of public health, in order to foresee and to conclude about which model is more suitable to forecast the monthly cost of antibiotics in hospital infections. The results obtained from the two models were considered satisfactory but the model of dynamic regression was chosen to be more suitable because it presented a mean absolute percentage error (MAPE). Finally, the findings might be a managerial tool for hospital administration when they offer subsides for the budget of planning and of the resource finances, especially in a time when resources are globally scarce, making health even more expensive.Os escassos recursos da saúde pública impõem ao administrador gerenciar a destinação dos recursos buscando racionalizar e otimizar sua alocação, permitindo, desta forma, melhorar o atendimento aos pacientes, pois o hospital, sendo uma entidade pública, não tem por objetivo o lucro, mas sim promover o bem estar da comunidade. Com isso, a infecção hospitalar que é adquirida após a internação do paciente e se manifesta durante a internação ou mesmo após a alta, podendo ser relacionada com a internação ou procedimentos hospitalares, deve ser evitada. Uma vez que sua total erradicação não é possível, se faz necessário analisar e controlar o custo mensal dos principais antibióticos utilizados no seu tratamento a fim de se ter embasamento suficiente para prever a alocação de recursos para sua aquisição. Nesse contexto, o principal objetivo desta pesquisa é realizar a previsão do custo mensal e de alocação de recursos necessários para aquisição de medicamentos utilizados no tratamento de infecções hospitalares no Hospital Universitário de Santa Maria. Para isso, utilizou-se a metodologia de previsão por regressão linear múltipla e de regressão dinâmica combinada com a técnica multivariada de componentes principais que foi utilizada para eliminar a multicolinearidade existente entre as variáveis originais. Com isso, as componentes principais resultantes foram utilizadas como variáveis independentes na construção do modelo de regressão linear múltipla e de regressão dinâmica. Portanto, essas metodologias são aplicadas a um estudo de caso na saúde pública, a fim de fazer previsões e tirar conclusões a respeito de qual modelo é mais adequado para realizar a previsão do custo mensal dos antibióticos em infecções hospitalares. Os resultados obtidos nos dois modelos foram considerados satisfatórios, mas foi escolhido, como modelo mais adequado para realizar as previsões, o modelo de regressão dinâmica, porque apresentou o menor erro percentual absoluto médio (MAPE). Por fim, as previsões encontradas, podem se constituir em uma ferramenta gerencial para a administração hospitalar ao fornecer subsídios para o planejamento orçamentário e financeiro dos recursos, especialmente em uma época em que há escassez de recursos em escala global, com reflexos muito intensos nos custos da saúde.Universidade Federal de Santa MariaBREngenharia de ProduçãoUFSMPrograma de Pós-Graduação em Engenharia de ProduçãoSouza, Adriano Mendonçahttp://lattes.cnpq.br/5271075797851198Zanini, Roselaine Ruviarohttp://lattes.cnpq.br/4332331006565656Silva, Wesley Vieira dahttp://lattes.cnpq.br/1710286275396858Bastos, Claudio2009-11-242009-11-242009-09-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfBASTOS, Claudio. Resource collection for anti-microbial at the University Hospital of Santa Maria by means of forecasts. 2009. 83 f. Dissertação (Mestrado em Engenharia de Produção) - Universidade Federal de Santa Maria, Santa Maria, 2009.http://repositorio.ufsm.br/handle/1/8122porinfo:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2023-04-10T18:45:25Zoai:repositorio.ufsm.br:1/8122Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2023-04-10T18:45:25Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria Resource collection for anti-microbial at the University Hospital of Santa Maria by means of forecasts |
title |
Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria |
spellingShingle |
Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria Bastos, Claudio Regressão linear múltipla Regressão dinâmica Análise de componentes principais Custos em saúde Multiple linear regression Dynamic regression Principal components analysis CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO |
title_short |
Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria |
title_full |
Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria |
title_fullStr |
Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria |
title_full_unstemmed |
Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria |
title_sort |
Modelos de previsão de recursos para antimicrobianos no Hospital Universitário de Santa Maria |
author |
Bastos, Claudio |
author_facet |
Bastos, Claudio |
author_role |
author |
dc.contributor.none.fl_str_mv |
Souza, Adriano Mendonça http://lattes.cnpq.br/5271075797851198 Zanini, Roselaine Ruviaro http://lattes.cnpq.br/4332331006565656 Silva, Wesley Vieira da http://lattes.cnpq.br/1710286275396858 |
dc.contributor.author.fl_str_mv |
Bastos, Claudio |
dc.subject.por.fl_str_mv |
Regressão linear múltipla Regressão dinâmica Análise de componentes principais Custos em saúde Multiple linear regression Dynamic regression Principal components analysis CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO |
topic |
Regressão linear múltipla Regressão dinâmica Análise de componentes principais Custos em saúde Multiple linear regression Dynamic regression Principal components analysis CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO |
description |
The scarce resources of public health makes the administrator manage the destination of resources, aiming to rationalize and optimize its collection, in order to improve the assistance to patients because the hospital is a public institution and does not get profits but promotes the community well-being. Thus, the hospital infection is acquired after the patient comes to the hospital of after he goes home and might be associated with his staying in hospital or with hospital procedures. This cost must be avoided. Once the complete eradication is not impossible, it is necessary to analyze and to control the monthly cost of the main antibiotics used for its treatment so that there is enough knowledge to foresee the resource collection to buy them. In this context, the main reason of this research is to carry out a forecast of the monthly cost and of the resource collection needed to purchase those medicine used in the treatment of hospital infections at the University Hospital of Santa Maria. To do so, a methodology for forecast by dynamic and multiple linear regressions was used. They were combined with to a multivariate technique by principal components. The technique of principal components was used to eliminate the multiple linearity existing among the original variants so, the resulting principal components were used as variables in the construction of the model of multiple linear regression and of dynamic regression. Therefore, these methodologies are applied to a case study of public health, in order to foresee and to conclude about which model is more suitable to forecast the monthly cost of antibiotics in hospital infections. The results obtained from the two models were considered satisfactory but the model of dynamic regression was chosen to be more suitable because it presented a mean absolute percentage error (MAPE). Finally, the findings might be a managerial tool for hospital administration when they offer subsides for the budget of planning and of the resource finances, especially in a time when resources are globally scarce, making health even more expensive. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-11-24 2009-11-24 2009-09-04 |
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 |
BASTOS, Claudio. Resource collection for anti-microbial at the University Hospital of Santa Maria by means of forecasts. 2009. 83 f. Dissertação (Mestrado em Engenharia de Produção) - Universidade Federal de Santa Maria, Santa Maria, 2009. http://repositorio.ufsm.br/handle/1/8122 |
identifier_str_mv |
BASTOS, Claudio. Resource collection for anti-microbial at the University Hospital of Santa Maria by means of forecasts. 2009. 83 f. Dissertação (Mestrado em Engenharia de Produção) - Universidade Federal de Santa Maria, Santa Maria, 2009. |
url |
http://repositorio.ufsm.br/handle/1/8122 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria BR Engenharia de Produção UFSM Programa de Pós-Graduação em Engenharia de Produção |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria BR Engenharia de Produção UFSM Programa de Pós-Graduação em Engenharia de Produção |
dc.source.none.fl_str_mv |
reponame:Manancial - Repositório Digital da UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Manancial - Repositório Digital da UFSM |
collection |
Manancial - Repositório Digital da UFSM |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
_version_ |
1805922119541850112 |