Eduardo Cabrera

Simulation Optimization for Healthcare Emergency Departments

Abstract 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.

An Agent-Based Decision Support System for Hospitals Emergency Departments *

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.

Optimization of Healthcare Emergency Departments by Agent-Based Simulation☆

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.

ABMS optimization for emergency departments

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.

Using an Agent-based Simulation for Predicting the Effects of Patients Derivation Policies in Emergency Departments

Abstract 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.

Quantitative Evaluation of Decision Effects in the Management of Emergency Department Problems

Abstract Due to the complexity and crucial role of an Emergency Department(ED) in the healthcare system. The ability to more accurately represent, simulate and predict performance of ED will be invaluable for decision makers to solve management problems. One way to realize this requirement is by modeling and simulating the emergency department, the objective of this research is to design a simulator, in order to better understand the bottleneck of ED performance and provide ability to predict such performance on defined condition. Agent-based modeling approach was used to model the healthcare staff, patient and physical resources in ED. This agent-based simulator provides the advantage of knowing the behavior of an ED system from the micro-level interactions among its components. The model was built in collaboration with healthcare staff in a typical ED and has been implemented and verified in a Netlogo modeling environment. Case studies are provided to present some capabilities of the simulator in quantitive analysis ED behavior and supporting decision making. Because of the complexity of the system, high performance computing technology was used to increase the number of studied scenarios and reduce execution time.

Optimization of emergency departments by agent-based modeling and simulation

This article presents an agent-based modeling and simulation to design a decision support system for healthcare emergency department 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 University Autonoma of Barcelona with close collaboration of the hospital staff team. The objective of the proposed ABM procedure is to optimize the performance of such complex and dynamic healthcare EDs. 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 problem. An index is proposed to minimize patient length of stay in the ED. The results obtained by using alternatives Monte Carlo and Pipeline schemes are promising and a better understanding of the problem is achieved. The impact of such schemes to reduce the computational cost of exhaustive search is outlined.

Simulation and Big Data: A Way to Discover Unusual Knowledge in Emergency Departments: Work-in-Progress Paper

Here a work in progress is reported on within research that aims to obtain knowledge about variables which may influence a hospital emergency departments performance and quality of service. Knowledge discovery will be achieved through the analysis of intensive data generated by the simulation of any possible scenario in the real system. The challenge is to provide knowledge of critical, non-usual or extreme situations. Simulation is the only way to obtain information about these kinds of situations, as it is not possible to test such scenarios in the real system. We show how simulation of the real system through advanced computing is a source of big data, as it allows rapid and massive data generation. The potential of high performance computing makes it possible to generate a very large amount of data within a reasonable time, store this data, then process and analyze it to obtain knowledge. We describe the methodology proposed for this goal, which is based on the use of the simulator as a sensor of the real system, and so as the main source of data. The application of data mining techniques will open the doors to knowledge. To verify that the proposed methodology works, we propose a case study in which the aim is to obtain knowledge from a set of data already available, obtained from the simulation of a reduced set of scenarios of the real system.

Simulating the micro-level behavior of emergency department for macro-level features prediction

Emergency departments are currently facing major pressures due to rising demand caused by population growth, aging and high expectations of service quality. With changes continuing to challenge healthcare systems, developing solutions and formulating policies require a good understanding of the complex and dynamic nature of the relevant systems. However, as a typically complex system, it is hard to grasp the non-linear association between macro-level features and micro-level behavior for a systematic understanding. Instead of describing all the potential causes of this complex issue, in this paper we present a layer-based application framework to discover knowledge of an emergency department system through simulating micro-level behaviors of its components to facilitate a systematic understanding. Finally, case studies are used to demonstrate the potential use of the proposed approach. Results show that the proposed framework can significantly rtheeflect the non-linear association between micro-level behavior and macro-level features.

A decision support system for hospital emergency departments designed using agent-based modeling and simulation

This paper presents the results of an ongoing project whose objective is to develop a model and a simulation that, used as Decision Support System, aid the heads of Hospital Emergency Departments to make the best informed decisions possible. The defined model is a pure Agent-Based Model, formed entirely of the rules governing the behavior of the agents that populate the system. Two distinct types of agents have been identified, active and passive. Active agents represent persons, whereas passive agents represent services and reactive systems. The actions of agents and the communication among them are represented using Moore state machines. The model also includes the environment in which agents move and interact. The simulation has been implemented using NetLogo, and it has been used to evaluate the potential benefits for the ED of the derivation to primary care services of those patients who attend emergency services without requiring an urgent attention.