Decision support tool for hospital resource allocation during the COVID-19 pandemic

The SARS-CoV-2 (COVID-19) pandemic has placed unprecedented demands on entire health systems and driven them to their capacity, so that health care professionals have been confronted with the difficult problem of ensuring appropriate staffing and resources to a high number of critically ill patients...

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Bibliographic Details
Published in:Informatics in medicine unlocked 2021, Vol.24, p.100618-100618, Article 100618
Main Authors: Brüggemann, Sven, Chan, Theodore, Wardi, Gabriel, Mandel, Jess, Fontanesi, John, Bitmead, Robert R.
Format: Article
Language:English
Subjects:
Contingency planning
COVID-19
Emergency planning
Ensemble simulation
Erlang distribution
In-house patient modeling
Real-time resource planning
Resource allocation
SARS-CoV-2
Scenario simulation
Staffing
Web application
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Summary:The SARS-CoV-2 (COVID-19) pandemic has placed unprecedented demands on entire health systems and driven them to their capacity, so that health care professionals have been confronted with the difficult problem of ensuring appropriate staffing and resources to a high number of critically ill patients. In light of such high-demand circumstances, we describe an open web-accessible simulation-based decision support tool for a better use of finite hospital resources. The aim is to explore risk and reward under differing assumptions with a model that diverges from most existing models which focus on epidemic curves and related demand of ward and intensive care beds in general. While maintaining intuitive use, our tool allows randomized “what-if” scenarios which are key for real-time experimentation and analysis of current decisions’ down-stream effects on required but finite resources over self-selected time horizons. While the implementation is for COVID-19, the approach generalizes to other diseases and high-demand circumstances.
ISSN:2352-9148
2352-9148
DOI:10.1016/j.imu.2021.100618