Hospitals faced unprecedented demand during the COVID-19 pandemic, which significantly impacted their ability to efficiently allocate resources and ensure timely patient access to care. To assess hospital accessibility in Texas, we measured hospital drivetime access using publicly available weekly hospitalization rates, staffed bed supply, and demand during the pandemic. We utilized private hospital inpatient records from the Texas Department of Health and Human Services to determine the likely ZIP code origin of patients and estimate their drive times to care (cost). These estimates represent the cost of accessing care based on proximity, taking into account events such as traffic congestion and road conditions. Using Python’s geopandas library, we implemented the Enhanced Two-Step Floating Catchment Area (E2SFCA) and the Two-Step Floating Catchment Area (2SFCA) model. These models estimate patient flow and hospital access scores by analyzing fluctuations in the relationship between hospital supply and patient demand on a weekly basis. We compared how these scores change based on variations in available hospital beds, patient population density, and weekly demand. these models to assess their predictive accuracy in patient hospital choice. The results revealed that access is influenced by the proximity of patients to hospitals and estimating demand is important in the allocation of resources. The results from this study can be used to inform healthcare decision-making during times of increased patient volume, such as during pandemics, by identifying areas of high demand with limited resources. Future work will aim to refine this model by using more detailed cost matrices, testing it on other states, and applying it over longer periods of time.Integrative Biolog
