Yoğun bakım ünitelerinde tekrar denemeli hasta kabul ve taburcu etme kontrollerin incelenmesi

Intensive Care Units (ICUs) are scarce resources and operate most of the time under high occupancy rates. When faced with limited bed availability, arriving patients are sometimes refused or admitted by discharging an existing patient early, which may result in both increased readmission rates and patient/hospital related costs. Yet, there is not a well-defined admission and discharge control policy to decrease such adverse consequences. In this thesis, we consider a conceptual ICU setting that serves multiple types of patients, which reflects the trade-offs between first-time and recurring patients, between different health stages as well as between the early-discharge and rejection decisions. To do this, we define a so-called readmission orbit whose population consists of patients who are to be readmitted after a previous ICU discharge. There are two major approaches available to represent and analyze such a model: Markov Decision Processes (MDPs) and fluid approximations. Since the conceptual model suffers from the curse of dimensionality, we first consider a simple version of the conceptual model with only one patient type and one health condition, by which we isolate the effects various admission and discharge decisions on readmissions. We present a discrete-time MDP formulation for the simple model and investigate the structure of the optimal admission and discharge control policy under such a model. Next, we present discrete-time MDP formulation for the conceptual model, which aims to develop a well-performing and implementable policy, rather than finding an optimal policy. Then, we develop a deterministic fluid model to control admissions and discharges in such an environment and show that the optimal control of the fluid model in the steady state can be obtained by solving a nonlinear problem. We derive a heuristic policy based on the solution of this nonlinear problem. Finally, we develop a discrete-event simulation platform that represents the ICU system. The platform is quite general, so that it can represent ICUs with different characteristics as well as a variety of control policies. We use this platform to benchmark the performance of the proposed heuristic policies, with those of the two state-of-the-art policies. Numerical results show that our proposed policies significantly outperform the the state-of-the-art policies.