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An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system.
Chertok, Daniel; Konchak, Chad; Shah, Nirav; Singh, Kamaljit; Au, Loretta; Hammernik, Jared; Murray, Brian; Solomonides, Anthony; Wang, Ernest; Halasyamani, Lakshmi.
Afiliação
  • Chertok D; NorthShore University HealthSystem, Evanston, Illinois, United States of America.
  • Konchak C; NorthShore University HealthSystem, Evanston, Illinois, United States of America.
  • Shah N; NorthShore University HealthSystem, Evanston, Illinois, United States of America.
  • Singh K; University of Chicago Pritzker School of Medicine, Chicago, Illinois, United States of America.
  • Au L; NorthShore University HealthSystem, Evanston, Illinois, United States of America.
  • Hammernik J; NorthShore University HealthSystem, Evanston, Illinois, United States of America.
  • Murray B; NorthShore University HealthSystem, Evanston, Illinois, United States of America.
  • Solomonides A; NorthShore University HealthSystem, Evanston, Illinois, United States of America.
  • Wang E; NorthShore University HealthSystem, Evanston, Illinois, United States of America.
  • Halasyamani L; NorthShore University HealthSystem, Evanston, Illinois, United States of America.
PLoS One ; 16(10): e0258710, 2021.
Article em En | MEDLINE | ID: mdl-34669732
An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Assistência Integral à Saúde / Pandemias / SARS-CoV-2 / COVID-19 / Modelos Teóricos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Assistência Integral à Saúde / Pandemias / SARS-CoV-2 / COVID-19 / Modelos Teóricos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article