A Queueing Network Model for Analysis of Patient Transitions Within Hospitals

Safe and efficient patient transitions are of critical importance to ensure patient safety and care quality. To study patient transitions, this paper presents a queueing network model-based iteration method to model and analyze transitions between emergency department, intensive care unit, and general ward within a hospital. Routings with feedback flows are considered under general arrival and service processes, and the effects of blocking on performance measures are presented for both the mean and variability. It is shown that the iteration procedure is convergent and leads to acceptable accuracy of estimation, for both small- and large-sized hospitals. In addition, the impacts of bed capacity, admission rate, as well as arrival and service time variabilities are discussed. Such a method provides an efficient way to study patient transitions within hospitals. Note to Practitioners —Many critical and complex problems occur at the interfaces of healthcare delivery systems. Within hospitals, transitions occur when patients are transferred between units such as emergency department, intensive care unit, and general ward. Although numerous studies on patient transitions have been carried out, most of them only focus on a single unit or transition without recognizing the importance of coordination among multiple units. However, understanding the complete patient flow throughout the hospital is crucial for hospital administrators in predicting the expected demand and planning resources appropriately. Thus, a quantitative model to study the transitions within the hospital by taking into account the interactions among all the involved departments is needed. In this paper, we present a queueing network model to evaluate patient transitions within hospitals. An iterative method is used to analyze transitions encompassing multiple departments. It is shown that such a method can result in close estimates of transition behavior and provide managerial insights to improve system performance. Moreover, the method is computationally efficient that it can be used to study very large-sized hospitals.