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Safety-Stock: Predicting the demand for supplies in Brazilian hospitals during the COVID-19 pandemic.
Gonzatto Junior, Oilson Alberto; Nascimento, Diego Carvalho; Russo, Cibele Maria; Henriques, Marcos Jardel; Tomazella, Caio Paziani; Santos, Maristela Oliveira; Neves, Denis; Assad, Diego; Guerra, Rafaela; Bertazo, Evelyn Keise; Cuminato, José Alberto; Louzada, Francisco.
Afiliação
  • Gonzatto Junior OA; Department of Applied Mathematics and Statistics, Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil.
  • Nascimento DC; Department of Informatics Engineering and Computer Science, University of Atacama, Copiapó, Chile.
  • Russo CM; Department of Mathematics, University of Atacama, Copiapó, Chile.
  • Henriques MJ; Department of Applied Mathematics and Statistics, Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil.
  • Tomazella CP; Department of Applied Mathematics and Statistics, Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil.
  • Santos MO; Department of Informatics Engineering and Computer Science, University of Atacama, Copiapó, Chile.
  • Neves D; Department of Applied Mathematics and Statistics, Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil.
  • Assad D; Department of Applied Mathematics and Statistics, Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil.
  • Guerra R; Bionexo, São Paulo, Brazil.
  • Bertazo EK; Bionexo, São Paulo, Brazil.
  • Cuminato JA; Bionexo, São Paulo, Brazil.
  • Louzada F; Bionexo, São Paulo, Brazil.
Knowl Based Syst ; 247: 108753, 2022 Jul 08.
Article em En | MEDLINE | ID: mdl-35469240
Many challenges lie ahead when dealing with COVID-19, not only related to the acceleration of the pandemic, but also to the prediction of personal protective equipment sets consumption to accommodate the explosive demand. Due to this situation of uncertainty, hospital administration encourages the excess stock of these materials, over-stocking products in some hospitals, and provoking shortages in others. The number of available personal protective equipment sets is one of the three main factors that limit the number of patients at a hospital, as well as the number of available beds and the number of professionals per shift. In this scenario, we developed an easy-to-use expert system to predict the demand for personal protective equipment sets in hospitals during the COVID-19 pandemic, which can be updated in real-time for short term planning. For this system, we propose a naive statistical modeling which combines historical data of the consumption of personal protective equipment sets by hospitals, current protocols for their uses and epidemiological data related to the disease, to build predictive models for the demand for personal protective equipment in Brazilian hospitals during the pandemic. We then embed this modeling in the free Safety-Stock system, which provides useful information for the hospital, especially the safety-stock level and the prediction of consumption/demand for each personal protective equipment set over time. Considering our predictions, a hospital may have its needs related to specific personal protective equipment sets estimated, taking into account its historical stock levels and possible scheduled purchases. The tool allows for adopting strategies to control and keep the stock at safety levels to the demand, mitigating the risk of stock-out. As a direct consequence, it also enables the interchange and cooperation between hospitals, aiming to maximize the availability of equipment during the pandemic.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies País/Região como assunto: America do sul / Brasil Idioma: En Revista: Knowl Based Syst Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies País/Região como assunto: America do sul / Brasil Idioma: En Revista: Knowl Based Syst Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Holanda