Capacity planning and admission control policies for intensive care units

Abstract

Poor management of the patient flow in intensive care units (ICUs) causes service rejections and presents significant challenges from the standpoint of capacity planning and management in ICUs. This thesis reports on the development of a simulation framework to study admission control polices that aim to decrease the rejection rate in the ICU at Children's Hospital Boston (CHB), and to provide predictions for the future state of the ICU system. To understand the patient flow process, we extensively analyze the arrival and length of stay (LOS) data from the ICU census. The simulation model for the ICU is developed based on the results from this statistical analysis as well as the currently-practiced scheduling and admission policies of the ICU at CHB. The model is validated to provide accurate estimates for important performance metrics such as rejection rates in the ICU. The simulation model is used to study the performance of many admission control policies. The policies of our interest exploit "caps" to control the number of scheduled patients who are allowed to enter the ICU on a single day. In particular, we consider two cap-based policies: the uniform cap policy (UCP), which is the existing policy in CHB, and the service-specific cap policy (SSCP), which is originally proposed in this thesis. While the UCP implements caps on the total census of surgical patients, the SSCP utilizes the service-oriented heterogeneity of surgical patients' LOS and enforces caps on separate groups of surgical patients based on their average LOS. We show that the UCP can reduce the rejection rate in the ICU at the expense of extra waiting time of scheduled patients. The SSCP is shown to further decrease the rejection rate while increasing the waiting time compared to the UCP. We also demonstrate that the performance of both policies depends on the level of system utilization. In order to validate our results theoretically, a discrete-time queueing model for the ICU is developed and verified to provide estimates for performance measures that are consistent with the results from simulation. Finally, we introduce the notion of state-dependent prediction, which aims to identify the likelihood of the future state of the ICU conditional on the information of a current state. Several experiments are conducted by simulation to study the impact of a current state on a state in the future. According to our results, current state information can be useful in predicting the state of the ICU in the near future, but its impact gradually diminishes as the time difference between the present and future grows. Our major finding is that the probability of unit saturation at a certain future time can be determined almost entirely by the number of current patients who will leave the ICU after that time, regardless of the total number of patients who are currently staying in the unit. These results imply the potential development of adaptive cap-based policies that dynamically adjust caps according to the outcomes of state-dependent predictions.