Francisco Epelde

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.

Evaluación del efecto de la información en la satisfacción de los pacientes atendidos en los servicios de urgencias hospitalarios

Resumen Fundamento La calidad percibida por los pacientes en los servicios de urgencias esta condicionada por varios factores entre los que la informacion suministrada en relacion con su proceso asistencial y la entregada en forma de instrucciones al alta es uno de los elementos mas apreciados por los pacientes. Se plantea como objetivo del estudio la valoracion del efecto en la calidad percibida por el usuario de una intervencion consistente en la entrega de instrucciones estructuradas en el momento del alta. Material y metodo Se realiza un estudio multicentrico en 33 centros, en pacientes con afeccion traumatica menor atendidos en servicios de urgencias hospitalarios. Se establecen dos fases: en la primera se mantiene la pauta asistencial ordinaria utilizada como control, y en la segunda, se aporta informacion mediante la entrega de instrucciones estructuradas al alta. Se realiza una encuesta telefonica a los 3 dias y se valora mediante escala numerica del 0 al 10 la calidad percibida por los pacientes. Resultados En la valoracion basal la puntuacion media fue de 8,1 frente a 8,3 en la segunda fase con diferencia estadistica (p Conclusiones La entrega de instrucciones al alta y su explicacion mejora la satisfaccion del usuario, mas en aquellos cuyo resultado asistencial ha sido favorable.

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.

A simulation and optimization based method for calibrating agent-based emergency department models under data scarcity

A method to calibrate an emergency department model with incomplete data.Simulation-based optimization for model parameter calibration.An accurate emergency department simulator. To tackle the problem of efficiently managing increasingly complex systems, simulation models have been widely used. This is because simulation is safer, less expensive, and faster than field implementation and experimenting. To achieve high fidelity and credibility in conducting prediction and exploration of the actual system with simulation models, a rigorous calibration and validation procedure should firstly be applied. However, one of the key issues in calibration is the acquisition of valid source information from the target system. The aim of this study is to develop a systematic method to automatically calibrate a general emergency department model with incomplete data. The simulation-based optimization was used to search for the best value of model parameters. Then we present a case study to particularly demonstrate the way to calibrate an agent-based model of an emergency department with real data scarcity. The case study indicates that the proposed method appears to be capable of properly calibrating and validating the simulation model with incomplete data.

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.

El infarto agudo de miocardio en los servicios de urgencias hospitalarios españoles. El estudio EVICURE

No published information is available about myocardial infarction management in Spanish emergency departments. The EVICURE is a prospective, multicenter, observational study involving 35 hospitals that for a 3-week period collected all the cases of patients requiring care in which the final diagnosis of the cause of symptoms was ischemic heart disease. The study included 2,216 patients, of which 600 (27.1%) with acute myocardial infarction formed the study population. Fifteen patients died in the emergency department (2.5%) and 80 (13.3%) diagnosed as myocardial infarction were admitted to the ward instead of the coronary care unit. The median time before patients were admitted to the coronary care unit was 32 minutes versus a median time of 111 minutes for all patients. Before leaving the emergency room, 461 patients (76.5%) received aspirin and 93 (15.5%) underwent fibrinolysis. We concluded that there is room for improvement in light of current standards of care.