Hospital emergency departments (EDs) are a primary healthcare unit, usually at the main entrance to the hospital and a key component of the whole healthcare system. The increasing demand of urgent care and the overcrowding of EDs are phenomena shared by health systems around the world. A naive solution could be to increase the size of EDs, but this is not a viable option due to the worldwide limitation of operational budgets. Resource planning in EDs is complex because its activity varies depending on time, day of week and season. So the ability to manage both regular and special situations, such as seasonal or unexpected increases in ED demand, is the key for the efficient use of resources. Simulation enables us to answer questions like "what happens if?" (e.g., in case of a specific pandemic, to explore how the composition of ED staff members influences the number of patients attended in a period of time) and to find the answer to questions such as "which is the best for ...?" (e.g., the number of healthcare staff that leads to minimizing the "Length of Stay" of patients, constrained by the availability of budget and number of healthcare staff), the optimization is needed. Discrete Event Simulation (DES), System Dynamics (SD) and Agent-Based Modeling and Simulation (ABMS) are the main approaches used in the modeling and simulation of healthcare systems. A basic survey of these approaches, describing the characteristics of each one of them, including their pros and cons, allows us to show the advantages of the ABMS strategy. The defined ED model is a pure Agent-Based Model, formed entirely by the rules governing the behavior of the individual agents which populate the system. Two distinct types of agents have been identified, active and passive. Active agents represent the persons involved in the ED (patients and ED staff), whereas passive agents represent services and other reactive systems, such as the information technology infrastructure or services used for performing tests. Each agent (individual) is represented through a state machine. This includes all the required variables to represent the many different "states" in which an agent may be during the time it is in the ED. The changes of these variables, fired by inputs received from other agents, generate the states' transition. For modeling interaction between agents, the model includes communication between individuals and the physical environment in which agents interact (admissions, triage booths, waiting rooms, or consultation suites). Using the simulation environment Netlogo, the simulation and optimization tools have been developed to be integrated in a Decision Support System (DDS), in order to help the managers to enhance the operation of an ED, providing additional knowledge for deciding on strategies such as patient admission scheduling, healthcare staff composition or clinical resources available. The DSS can be applied in different EDs, after a previous adjustment of the configuration parameters achieved through a tuning process. This research has been developed in collaboration with staff members of the Parc Tauli Hospital in Sabadell. © 2013 Nova Science Publishers, Inc. All rights reserved.
|Title of host publication||Advances in Computational Modeling Research: Theory, Developments and Applications|
|Number of pages||30|
|Publication status||Published - 1 Dec 2013|