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

Abstract

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.

Keywords

• Real-time resource planning
• COVID-19
• SARS-CoV-2
• In-house patient modeling
• Resource allocation
• Staffing