Decision Support System Based on Distributed Simulation Optimization for Medical Resource Allocation in Emergency Department

摘要

The number of emergency cases or people making emergency room visit has rapidly increased annually, leading to an imbalance in supply and demand, as well as long-term overcrowding of emergency departments (EDs) in hospitals. However, solutions targeting the increase of medical resources and improving patient needs are not practicable or feasible in the environment in Taiwan. Therefore, under the constraint of limited medical resources, EDs must optimize medical resources allocation to minimize the patient average length of stay (LOS) and medical resource wasted costs (MWCs). This study constructs a mathematical model for medical resource allocation of EDs, according to emergency flow or procedures. The proposed mathematical model is highly complex and difficult to solve because its performance value is stochastic and it considers both objectives simultaneously. Thus, this study postulates a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm Ⅱ (NSGA Ⅱ) and multi-objective computing budget allocation (MOCBA), and constructs an ED simulation model to address the challenges of multi-objective medical resource allocation. Specifically, the NSGA Ⅱ entails investigating plausible solutions for medical resource allocation, and the MOCBA involves identifying effective sets of feasible Pareto medical resource allocation solutions and effective allocation of simulation or computation budgets. Additionally, the discrete simulation model of EDs estimates the expected performance value. Furthermore, based on the concept of private cloud, this study presents a distributed simulation optimization framework to reduce simulation time and subsequently obtain simulation outcomes more rapidly. This framework assigns solutions to different virtual machines on separate computers to reduce simulation time, allowing rapid retrieval of simulation results and the collection of effective sets of optimal Pareto medical resource allocation solutions. Finally, this research constructs an ED simulation model based on the ED of a hospital in Taiwan, and determines the optimal ED resource allocation solution by using the simulation model and algorithm. The effectiveness and feasibility of this method are identified by conducting the experiment, and the experimental analysis proves that the proposed distributed simulation optimization framework can effectively reduce simulation time.