Creating a SimNICU: Using SAS ® Simulation Studio to Model Staffing Needs in Clinical Environments

Patient safety in a neonatal intensive care unit (NICU)—as in any hospital unit—is critically dependent on appropriate staffing. This project used SAS ® Simulation Studio to create a discrete-event simulation model of a specific NICU that can be used to predict the number of nurses needed per shift. The model incorporates the complexities inherent in determining staffing needs, including variations in patient acuity, referral patterns, and length of stay. To build the model, the group first estimated probability distributions for the number and type of patients admitted each day to the unit. Using both internal and published data, the team estimated distributions for various NICU-specific patient morbidities, including type and timing of each morbidity event and its temporal effect on a patient’s acuity. The final simulation model samples from these input distributions and simulates the flow of individual patients through the NICU (consisting of critical-care and step-down beds) over a one-year period. The general basis of the model represents a method that can be applied to any unit in any hospital, thereby providing clinicians and administrators with a tool to rigorously and quantitatively support staffing decisions. With additional refinements, the use of such a model over time can provide significant benefits in both patient safety and operational efficiency