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Forecast UTI: application for predicting Intensive Care Units beds in the context of the covid-19 pandemic / Forecast UTI: aplicación para pronosticar lechos de Unidades de Cuidados Intensivos en el contexto de la pandemia de covid-19 / Forecast UTI: aplicativo para previsão de leitos de unidades de terapia intensiva no contexto da pandemia de covid-19
Preprint
in Portuguese
| SciELO Preprints
| ID: pps-1034
Responsible library:
BR1.1
ABSTRACT
In view of the need to manage and forecast the number of beds in the Intensive Care Units for critically ill patients in Covid-19, the Forecast UTI was developed an open access application that allows the monitoring of hospital indicators based on historical data from the service health and the temporal dynamics of the epidemic. It is also possible to make short-term forecasts of the number of beds occupied daily by the disease and to establish possible care scenarios. This article presents the functions, mode of access and examples of use of Forecast UTI, a computational tool capable of assisting managers of public and private hospitals in the Unified Health System, since they support decision-making quickly, strategically and efficiently.
RESUMO
Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTI) para pacientes graves de covid-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso e que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal dessa epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo sobre número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente.
Full text:
Available
Collection:
Preprints
Database:
SciELO Preprints
Type of study:
Prognostic study
/
Risk factors
/
Screening study
Language:
Portuguese
Year:
2020
Document type:
Preprint
