Bed Allocation Optimization Based on Survival Analysis, Treatment Trajectory and Costs Estimations

Abstract

The intensive care units are a key element of patient flow, but due to high demand and an alternating rate of arriving patients, these units are often challenged by insufficient capacity, very high expenses, and in some cases, an unfair distribution of resources. Proper allocation of resources to match demand is, therefore, a vital task for many wards in these units. The patient bed assignment problem consists of managing in the best possible way a set of beds with equipment to be assigned to a particular type of patient. However, in real-world scenarios, constraints like a possible treatment trajectory are violated in most cases. In this paper, we present a new approach for solving patient bed assignment problems constrained by targets on survival function estimation, cost estimation, and possible treatment trajectory estimation for patients with cardiovascular diseases. For survival function estimations, we used the nave estimator and Kaplan-Meier, and for treatment effect estimations, we used logistic regression and T-learning. Estimations of the three components are used as weights in a genetic algorithm. This technique allows for the consideration of various constraints, which, unlike other techniques, allows for the selection of dominant solutions as solutions that satisfy dominant constraints. In addition, we demonstrate the robustness of our approach by testing the algorithms with multiple classes of patients, testing multiple sets of parameters, and comparing our results with several similar research studies showing the added value of working on this management axis in hospitals using the new approach to bed allocation.

Keywords

• Bed allocation
• costs
• multi-objectives optimization
• machine learning
• intensive care units
• survival analysis