Abstract
Background:We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is positive forcoronavirus disease 2019 (COVID-19) and the rate at which COVID-19 is spreading in the population. Our goal was to facilitate the implementation of data-driven recommendations for capacity management with a transparent mathematical simulation designed to answer the specific, local questions hospital leadership considered critical.Methods:The model facilitates hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool with modifiable parameters.Results:The use of the model is illustrated by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21. The model was used to inform COVID-19 planning and decision-making, including Intensive Care Unit (ICU) staffing and ventilator procurement.Conclusion:In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.Contributions to the literature:· Generation and implementation of data-driven recommendations for hospital capacity management early in the COVID-19 pandemic· The conceptualization, development, and deployment of an interactive simulation model in two weeks· Data-driven capacity management in the presence of significant uncertainty about the expected volume of patients, their clinical needs, and the availability of the workforceTrial Registration: Not applicable
A Model of Bed Demand to Facilitate the Implementation of Data-driven Recommendations for COVID-19 Capacity Management
