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A Queueing Model of Patient Flow for Stroke Networks to Estimate Acute Stroke Transfer Capacity

Background: Most acute stroke (AS) patients in the United States are initially evaluated at a primary stroke center (PSC) and a significant proportion requires transfer to a comprehensive stroke center (CSC) for advanced treatment. A CSC typically accepts patients from multiple PSCs in its network, leading to capacity limits. This study uses a queueing model to estimate impacts on CSC capacity due to transfers from PSCs. Methods: The model assumes that the number of AS patients arriving at each PSC, proportion of AS patients transferred, and length of stay in the CSC Neurologic Intensive Care Unit (Neuro-ICU) by type of AS are random, while the transfer rates of ischemic and hemorrhagic AS patients are control variables. The main outcome measure is the "overflow" probability, namely, the probability of a CSC not having capacity (unavailability of a Neuro-ICU bed) to accept a transfer. Data simulations of the model, using a base case and an expanded case, were performed to illustrate the effects of changing key parameters, such as transfer rates from PSCs and CSC Neuro-ICU capacity on overflow capacity. Results: Data simulations of the model using a base case show that an increase of a PSC's ischemic stroke transfer rate from 15% to 55% raises the overflow probability from 30.62% to 36.13%. Further simulations of the expanded case show that to maintain an a priori CSC overflow probability of 30.62% when adding a PSC with a AS transfer rate of 15% to the network, other PSCs would need to decrease their transfer rate by 12.5% or the CSC Neuro-ICU would need to add 2 beds. Discussion: A queuing model can be used to estimate the effects of change in the size of a PSC-CSC network, change in AS transfer rates, or change in number of CSC Neuro-ICU beds of a CSC on its capacity on the overflow probability in the CSC.