Projecting hospital resource utilization during a surge using parametric bootstrapping

During the initial wave of the COVID-19 pandemic in the United States, hospitals took drastic action to ensure sufficient capacity, including canceling or postponing elective procedures, expanding the number of available intensive care beds and ventilators, and creating regional overflow hospital capacity. However, in most locations the actual number of patients did not reach the projected surge leaving available, unused hospital capacity. As a result, patients may have delayed needed care and hospitals lost substantial revenue. These initial recommendations were made based on observations and worst-case epidemiological projections, which generally assume a fixed proportion of COVID-19 patients will require hospitalization and advanced resources. This assumption has led to an overestimate of resource demand as clinical protocols improve and testing becomes more widely available throughout the course of the pandemic. Here, we present a parametric bootstrap model for forecasting the resource demands of incoming patients in the near term, and apply it to the current pandemic. We validate our approach using observed cases at UCLA Health and simulate the effect of elective procedure cancellation against worst-case pandemic scenarios. Using our approach, we show that it is unnecessary to cancel elective procedures unless the actual capacity of COVID-19 patients approaches the hospital maximum capacity. Instead, we propose a strategy of balancing the resource demands of elective procedures against projected patients by revisiting the projections regularly to maintain operating efficiency. This strategy has been in place at UCLA Health since mid-April.