Agent-Based Modeling and Simulation (ABMS) has become a widely used approach for analyzing complex systems in multidisciplinary fields such as healthcare and hospital Emergency Departments (EDs). However, the adoption of this methodology is often hampered by monolithic implementations in which domain knowledge is tightly intertwined with computational logic, limiting the long-term reusability and adaptability of simulation models. Inspired by Lego®’s modularity, this paper presents a metasystem based on a modular architecture centered on an agent metagenerator. The proposed approach conceptually encapsulates the definitions of the agents in brick-style agents, decomposing each agent into six canonical blocks. These blocks are independent of the target programming language, ensuring a clear separation between conceptual specifications defined by domain experts and their computational implementation by engineers and technicians. This separation of concerns facilitates multidisciplinary collaboration by enabling experts to explicitly define agent behavior through standardized specifications. Unlike large-scale data-driven approaches, all agent decisions are explicitly defined and calibrated using a small, controlled dataset, preserving transparency and traceability between the conceptual model and the resulting computational behavior. The proposed metasystem is validated through a proof-of-concept implementation using a simplified ED case study. The results suggest that the architecture prevents re-monolithization while enhancing modularity, providing a solid foundation for reusable, traceable, and scientifically grounded ABMS.

Hospital Emergency Departments (EDs) are one of the more complex units of the healthcare system, requiring coordination of human and technical resources for managing situations effectively. This article establishes the basic principles to design primitives of an Agent-Based Modeling and Simulation (ABMS) modular system, inspired by the modularity of Lego blocks, which allows the creation of computational models that can be employed as Decision Support Systems (DSS). The ABMS modular system has shifted from a monolithic approach to an adjustable system. This means that the system allows the description of the metasystem and agent box used to build the computational models (simulator) that, used as DSS, can help EDs managers to achieve the highest possible level of service quality, given the available resources.

This paper presents a structured methodology for the development of agent-based modeling and simulation (ABMS) systems, aimed at improving modularity and reusability in complex system representations such as Lego® pieces. The methodology addresses the challenge of translating real-world problems into executable ABMS models by defining standardized tables for key model components: environment, agents, interactions, and decision logic. Each element is described using modular structures that facilitate both conceptual validation (by domain experts) and computational validation (through simulation testing). A case study focused on a simplified hospital Emergency Department (ED) illustrates the practical application of this framework, showcasing how agent behaviors and system dynamics can be clearly represented and adapted. The approach emphasizes reusability and automation potential, bridging the gap between conceptual design and code-level implementation.

Emergency Departments (EDs) face increasing complexity due to rising patient demand, resource constraints, and the need for efficient service coordination. Traditional simulation models, while useful, cannot be easily adapted to a different hospital environments, making it difficult to transfer and scale solutions. Based on previous work, with a simulator working in a hospital, this work describes a modular Agent-Based Modeling and Simulation (ABMS) approach for increasing flexibility adaptation and reuse in ED simulations. The proposed technique, which deconstructs existing models into individual components, will allow hospitals with different workflows and operational constraints to construct customized simulations. To validate this methodology, we develop a structured, modular framework using NetLogo and Python. The suggested metasystem enables adaptive simulation-based decision assistance for emergency departments, which improves resource allocation and operational planning.

Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory condition characterized by inflammation and narrowing of the airways, leading to symptoms such as shortness of breath, coughing, and chest tightness. Treatment typically involves lifestyle adjustments, medication, and pulmonary rehabilitation to improve lung function and quality of life. This study presents a model examining COPD patient behavior within cohort-based strategies, focusing on how environmental factors impact vital signs across the entire cohort. We developed a comprehensive virtual clinical trial model that encompasses study protocol design, participant recruitment, virtual data collection, outcome analysis, and conclusions. This includes remote symptom monitoring, virtual healthcare consultations, treatment adherence assessments, and research data collection. Additionally, we explore the influence of external variables such as environmental conditions, comorbidities, and lifestyle factors on chronic disease symptoms and disease stability. We used an Agent-Based Model(ABM) to incorporate these factors to assess COPD progression and treatment efficacy. Individual agents represent COPD patients, each characterized by attributes such as age, smoking history, lung function, comorbidities, and treatment plans.

Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory condition, ranking as the third leading cause of global morbidity and mortality. In this project, we simulate Real Clinical Trials using Virtual Clinical Trials (VCT) for COPD patients, offering possibilities not feasible in traditional trials, such as exploring treatment adherence levels and creating virtual cohorts with specific characteristics. We propose a cohort-based management strategy leveraging data analytics to identify patterns within the COPD patient population and advocate for employing a finite-state machine (FSM) approach to model COPD exacerbations. Further research and validation are crucial to refine and scale this integrated model.

The spread of COVID-19 between different agents in a hospital emergency department can be simulated by modeling the interactions between the agents and the environment. In this research, we use Agent Based Modeling and Simulation techniques to build a model of COVID-19 propagation based on an Emergency Department Simulator which has been tested and validated previously. The benefits of ABM include its ability to simulate complex systems, its flexibility, and its ability to model the interactions between different agents in the system. The obtained model will allow us to build a propagation simulator that enables us to build virtual environments with the aim of analyzing how the interactions between agents influence the rate of virus transmission. The model can be used to study the effectiveness of different interventions, such as social distancing, wearing masks, and vaccination, in reducing the spread of COVID-19.

Influenza is an acute viral infection that primarily attacks the upper respiratory tract. The disease occurs worldwide and spreads very quickly in populations, especially in crowded circumstances like Emergency Departments (ED). Influenza can be spread in three main ways, all of which are very feasible in ED environments: by the airborne route, by contaminated surfaces or from direct personal contact such as a handshake. Our research uses Agent Based Modeling and Simulation techniques to build the model and the simulation of the transmission of flu virus contact in ED. The simulator allows us to build virtual scenarios with the aim of understanding the phenomenon of flu transmission and the potential impact of the implementation of different policies in propagation rates. This work is an expansion of the previous one carried on by other members of our research group, with the aim of developing a more flexible and reasonable healthcare system simulation and

In healthcare environments we can find several microorganisms causing nosocomial infection, and of which one of the most common and most dangerous is Methicillin-resistant Staphylococcus Aureus. Its presence can lead to serious complications to the patient.Our work uses Agent Based Modeling and Simulation techniques to build the model and the simulation of Methicillin-resistant Staphylococcus Aureus contact transmission in emergency departments. The simulator allows us to build virtual scenarios with the aim of understanding the phenomenon of MRSA transmission and the potential impact of the implementation of different measures in propagation rates.

The saturation of Emergency Departments, due to the increasing demand of the service, is a current problem in the healthcare system. We propose an analytical model to obtain information from data obtained through the simulation of a Hospital Emergency Department. The model defines how to calculate the theoretical throughput of a particular sanitary staff configuration, that is, the number of patients it can attend per unit time given its composition. This index is a reference to measure indicators concerning to performance and emergency response capacity of the system. The data for the analysis will be generated by the simulation of any possible scenario of the real system, taking into account all valid sanitary staff configurations and different number of patients entering into the emergency service.

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.

Here a work in progress is reported on within research that aims to obtain knowledge about variables which may influence a hospital emergency department's 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.

The nosocomial infection is a special kind of infection that is caused by microorganisms acquired inside a hospital. In the daily care process of an emergency department, the interactions between patients and sanitary staff create the environment for the transmission of such microorganisms. Rates of morbility and mortality due to nosocomial infections are important indicators of the quality of hospital work. In this research, we use Agent Based Modeling and Simulation techniques to build a model of Methicillin-resistant Staphylococcus Aureus propagation based on an Emergency Department Simulator which has been tested and validated previously. The model obtained will allow us to build a contact propagation simulator that enables the construction of virtual environments with the aim of analyzing how the prevention policies affect the rate of propagation of nosocomial infection.

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.

Objective: To design a model and a simulation tool that hospital emergency department directors can use as a decision-making aid for managing human and material resources and for planning the strategic deployment of resources during an expected pandemic. Methods: We used an iterative spiral approach to developing the model in 5 phases (systems analysis, model building, design and implementation of the simulation tool, verifying the simulator's functionality, and validation of the simulator against real emergency department data). After each iteration, we identified improvements to make before the next phase. The process was repeated until the model and the simulation tool behaved similarly to the real system. The data necessary to develop the model were obtained from 2 hospitals, one a tertiary care facility and the other a secondary one; the centers were visited and interviews were conducted with the directors, supervisors and other staff. Results: The model describes the complex dynamics of a hospital emergency department whose behavior results from the actions and interactions of agents (patients, physicians, nurses, managers and other staff); therefore the behavior of each type of agent had to be reflected in the model. The functioning of the model was verified for a simple department, with analysis of the effects that changes in emergency staff deployment and levels of experience would have on the department's output. Conclusion: The simulation tool proved useful as a decision-making aid, allowing the user to identify the optimal deployment of human resources for attending a specific number of patients. [Emergencias 2012;24:189-195]

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.

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.