Many intensive care units (ICUs) face overcrowding. One response to this overcrowding is to bump ICU patients to other departments of the hospital to make room for new patient arrivals. Such bumping clearly has the potential to reduce quality of care. In this paper we develop a stochastic model of a single ICU with patient bumping. The purpose of this model is to enable planners to predict performance, in terms of bumping, under differing arrival patterns and capacity. We develop a Markov chain model and a new aggregation-disaggregation algorithm for this problem that enables us to keep track of the time in system for each patient despite the high dimensionality of the problem. Our approach allows for more accurate modeling of the system than previous work that assumed an exponential distribution for length of stay (LOS). We also demonstrate the superior computational efficiency of our approach over the Gauss-Seidel iterative method for solving the Markov chain. Finally, we use the model to explore how different surgery schedules influence bumping rates.