This paper presents an Agent-Based modeling and simulation to design a decision support system (DSS) for the operation of Healthcare Emergency Departments (ED). This DSS aims to aid EDs managers in setting up strategies and management guidelines to optimize the operation of EDs. This ongoing research is being performed by the Research Group on Individual Oriented Modeling (IoM) of CAOS in the University Autonoma of Barcelona (UAB) in close collaboration with Hospital ED Staff. The simulation main objective is to optimize the performance of such complex and dynamic Healthcare ED. Optimization is performed to find the optimal ED staff configuration, which consists of doctors, triage nurses, and admission personnel, i.e. a multidimensional problem. Two different indexes, to minimize patient waiting time, and to maximize patient throughput, were proposed and tested and their results obtained appying an exhaustive search technique, yield promising results and better understanding of the problem

This article presents an Agent-Based modeling (ABM) simulation to design a decision support system (DSS) for Healthcare Emergency Department (ED). This DSS aims to aid EDs heads in setting up management guidelines to improve the operation of EDs. This ongoing research is being performed by the Research Group in Individual Oriented Modeling (IoM) at the University Autonoma of Barcelona (UAB) with close collaboration of Hospital ED Staff Team. The objective of the proposed ABM procedure is to optimize the performance of such complex and dynamic Healthcare EDs, because worldwide most of them are overcrowded, and unable to provide ad hoc care, quality and service. Exhaustive search (ES) optimization is used to find out the optimal ED staff configuration, which includes doctors, triage nurses, and admission personnel, i.e., a multidimensional problem. An index is proposed to minimize patient length of stay in the ED. The results obtained by using an alternative pipeline scheme to ES are promising and a better understanding of the problem is achieved. The impact of the pipeline scheme to reduce the computational cost of exhaustive search is outlined

Modeling and simulation have been shown to be useful tools in many areas of the Healthcare operational management, field in which there is probably no area more dynamic and complex than hospital emergency departments (ED). This paper presents the results of an ongoing project that is being carried out by the Research Group in Individual Oriented Modeling (IoM) of the University Autonoma of Barcelona (UAB) with the participation of Hospital of Sabadell ED Staff Team. Its general objective is creating a simulator that, used as decision support system (DSS), aids the heads of the ED to make the best informed decisions possible. The defined ED model is a pure Agent-Based Model, formed entirely of 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 human actors, meanwhile passive agents represent services and other reactive systems. The actions of agents and the communication between them will be represented using Moore state machines extended to include probabilistic transitions. The model also includes the environment in which agents move and interact. With the aim of verifying the proposed model an initial simulation has been created using NetLogo, an agent-based simulation environment well suited for modeling complex systems.

Hospital emergency departments are complex systems, characterized by uncertainty and variability, often operating with limited resources and high demands. Simulations of these departments have proven to be efficient tools in improving their performance in certain circumstances, however the human aspect is often overlooked in the application of modeling and simulation to this field. This paper describes an ongoing project which is applying agent-based modeling techniques to the simulation of hospital emergency departments. The work performed so far, including system analysis and a preliminary model, are shown and the advantages of this technique explored.

This article presents an agent-based modeling and simulation to design a decision support system for healthcare emergency department (ED) to aid in setting up management guidelines to improve it. This ongoing research is being performed by the Research Group in Individual Oriented Modeling at the Universitat Autònoma de Barcelona with close collaboration of the hospital staff team of Sabadell. The objective of the proposed procedure is to optimize the performance of such complex and dynamic healthcare EDs, which are overcrowded. Exhaustive search optimization is used to find the optimal ED staff configuration, which includes doctors, triage nurses, and admission personnel, i.e., a multi-dimensional and multi-objective problem. An index is proposed to minimize patient stay time in the ED. The model is implemented using NetLogo. The results obtained by using alternatives Monte Carlo and Pipeline schemes are promising. The impact of these schemes to reduce the computational resources used is described.

The increasing demand of urgent care, overcrowding of hospital emergency departments (ED) and limited economic resources are phenomena shared by health systems around the world. It is estimated that up to 50% of patients that are attended in ED have non complex conditions that could be resolved in ambulatory care services. The derivation of less complex cases from the ED to other health care devices seems an essential measure to allocate properly the demand of care service between the different care units. This paper presents the results of an experiment carried out with the objective of analyzing the effects on the ED (patients’ Length of Stay, the number of patients attended and the level of activity of ED Staff) of different derivation policies. The experiment has been done with data of the Hospital of Sabadell (a big hospital, one of the most important in Catalonia, Spain), making use of an Agent-Based model and simulation formed entirely of the rules governing the behaviour of the individual agents which populate the ED, and due to the great amount of data that should be computed, using High Performance Computing