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An Agile Systems Modeling Framework for Bed Resource Planning During COVID-19 Pandemic in Singapore.
Lam, Sean Shao Wei; Pourghaderi, Ahmad Reza; Abdullah, Hairil Rizal; Nguyen, Francis Ngoc Hoang Long; Siddiqui, Fahad Javaid; Ansah, John Pastor; Low, Jenny G; Matchar, David Bruce; Ong, Marcus Eng Hock.
Afiliação
  • Lam SSW; Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
  • Pourghaderi AR; Health Services Research Centre, Singapore Health Services, Singapore, Singapore.
  • Abdullah HR; SingHealth Duke NUS Academic Medical Centre, Health Services Research Institute, Singapore, Singapore.
  • Nguyen FNHL; Lee Kong Chian School of Business, School of Computing and Information Systems, Singapore Management University, Singapore, Singapore.
  • Siddiqui FJ; Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
  • Ansah JP; Health Services Research Centre, Singapore Health Services, Singapore, Singapore.
  • Low JG; SingHealth Duke NUS Academic Medical Centre, Health Services Research Institute, Singapore, Singapore.
  • Matchar DB; Department of Anesthesiology, Singapore General Hospital, Singapore, Singapore.
  • Ong MEH; Health Services Research Centre, Singapore Health Services, Singapore, Singapore.
Front Public Health ; 10: 714092, 2022.
Article em En | MEDLINE | ID: mdl-35664119
ABSTRACT

Background:

The COVID-19 pandemic has had a major impact on health systems globally. The sufficiency of hospitals' bed resource is a cornerstone for access to care which can significantly impact the public health outcomes.

Objective:

We describe the development of a dynamic simulation framework to support agile resource planning during the COVID-19 pandemic in Singapore. Materials and

Methods:

The study data were derived from the Singapore General Hospital and public domain sources over the period from 1 January 2020 till 31 May 2020 covering the period when the initial outbreak and surge of COVID-19 cases in Singapore happened. The simulation models and its variants take into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes in Singapore.

Results:

The models were calibrated against historical data for the Singapore COVID-19 situation. Several variants of the resource planning model were rapidly developed to adapt to the fast-changing COVID-19 situation in Singapore.

Conclusion:

The agility in adaptable models and robust collaborative management structure enabled the quick deployment of human and capital resources to sustain the high level of health services delivery during the COVID-19 surge.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2022 Tipo de documento: Article