A Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment.

This paper describes the development and application of an analytical solution to assist with inpatient flow and capacity management at Memorial Sloan Kettering Cancer Center (MSKCC) in New York City. We present a discrete-event simulation model that captures several key aspects of the complex patient flow patterns at MSKCC in the inpatient setting. The model captures the variation in admission patterns based on various patient cohorts and admit locations. The model also accounts for the variability in specialized care needs for distinct patient cohorts using categorical distributions. Durations for various patient flow states from admission till discharge are modeled as probability distributions. Key patient-and resource attributes are also incorporated to accurately capture the constraints affecting resource allocation. A comprehensive set of output metrics is used to validate the model, and to compare alternative scenarios. We present results for a scenario that tests the impact of resource allocation changes aimed at consolidating patients on certain floors based on the hospital department tasked with their inpatient care. Outputs for the scenario are compared with baseline using the following output metrics: mean bed utilization by floor, mean admit boarding times by service, proportion of home floor admissions by service, and wait times for step-down care beds. Our results show an estimated reduction in average admit wait times by 30 minutes or more across 4 inpatient services (an annual reduction of