Models of Emergency Departments for Reducing Patient Waiting Times

In this paper, we apply both agent-based models and queuing models to investigate patient access and patient flow through emergency departments. The objective of this work is to gain insights into the comparative contributions and limitations of these complementary techniques, in their ability to contribute empirical input into healthcare policy and practice guidelines. The models were developed independently, with a view to compare their suitability to emergency department simulation. The current models implement relatively simple general scenarios, and rely on a combination of simulated and real data to simulate patient flow in a single emergency department or in multiple interacting emergency departments. In addition, several concepts from telecommunications engineering are translated into this modeling context. The framework of multiple-priority queue systems and the genetic programming paradigm of evolutionary machine learning are applied as a means of forecasting patient wait times and as a means of evolving healthcare policy, respectively. The models' utility lies in their ability to provide qualitative insights into the relative sensitivities and impacts of model input parameters, to illuminate scenarios worthy of more complex investigation, and to iteratively validate the models as they continue to be refined and extended. The paper discusses future efforts to refine, extend, and validate the models with more data and real data relative to physical (spatial–topographical) and social inputs (staffing, patient care models, etc.). Real data obtained through proximity location and tracking system technologies is one example discussed

Using agent-based modelling and simulation to model performance measurement in healthcare

One of the priority areas of the UK healthcare system is urgent and emergency care, especially accident and emergency departments (A&E departments). Currently, there is much interest in studying the unintended consequences of the current UK healthcare performance system. Simulation modelling has been proved to be a useful tool for modelling different aspects of the healthcare systems, particularly those related to the performance of A&E departments. Most of the available literature on modelling A&E departments focus on supporting operational decision-making and planning in specific healthcare units to study particular problems such as staff scheduling, resource utilisation, and waiting time issues. That is, most simulation studies focus on analysing how different configurations of healthcare systems affect their performance. However, to our knowledge, few simulation studies focus on explaining how human behaviour affects the performance of the system, and very few have studied how, in turn, performance targets set for A&E departments affect human behaviour in healthcare systems. Some aspects of human behaviour have been incorporated within existing simulation models, though with limitations. In fact, most studies have aimed to study patients’ behaviour, and few have included some aspects of the behaviour of clinical staff. Here we consider how to model clinician behaviour in relation to the performance of A&E departments. This thesis presents an exploratory study of the use of agent-based modelling and simulation (ABMS) and discrete event simulation (DES) to demonstrate how to model clinician behaviour within an A&E department and how that behaviour is related to waiting time performance. Clinical behaviour, incorporated in the simulation models developed here, employs a framework called PECS that assumes that behaviour is influenced by Physical (P), Emotional (E), Cognitive (C) and Social (S) factors. A discussion of the advantages and limitations of the use of ABMS and DES to model such behaviour is included. The findings of this research demonstrate that ABMS is well suited to simulate human behaviour in an A&E department. However, it is not explicitly designed to model processes of complex operational and queue-based systems such as accident and emergency departments. In addition, this research work also demonstrates that DES is an adequate tool for modelling A&E’s processes and patient flows, that can, in fact, incorporate different aspects of human behaviour. Furthermore, the process of modelling human behaviour in DES is complex because, though most DES software allows the representation of reactive behaviour, they make it difficult to model other types of human behaviour The main contributions of this thesis are: 1) a comparison and evaluation of how suitable ABMS and DES are for modelling clinical behaviour, 2) an approach to model the relationship between human behaviour and waiting time performance, considering four aspects of human behaviour (physical, emotional, cognitive and social)

Modelling the contact propagation of nosocomial infection in emergency departments

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 areimportant indicators of the quality of hospital work. In this research, we use Agent Based Modeling and Simulation techniques to build a model of Methicillinresistant 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 theaim of analyzing how the prevention policies affect the rate of propagation of nosocomial infectionPeer Reviewe

Assessing vulnerability and modelling assistance: using demographic indicators of vulnerability and agent-based modelling to explore emergency flooding relief response

Flooding is a significant concern for much of the UK and is recognised as a primary threat by most local councils. Those in society most often deemed vulnerable: the elderly, poor or sick, for example, often see their level of vulnerability increase during hazard events. A greater knowledge of the spatial distribution of vulnerability within communities is key to understanding how a population may be impacted by a hazard event. Vulnerability indices are regularly used – in conjunction with needs assessments and on-the-ground research – to target service provision and justify resource allocation. Past work on measuring and mapping vulnerability has been limited by a focus on income-related indicators, a lack of consideration of accessibility, and the reliance on proprietary data. The Open Source Vulnerability Index (OSVI) encompasses an extensive range of vulnerability indicators supported by the wider literature and expert validation and provides data at a sufficiently fine resolution that can identify vulnerable populations. Findings of the OSVI demonstrate the potential cascading impact of a flood hazard as it impacts an already vulnerable population: exacerbating pre-existing vulnerabilities, limiting capabilities and restricting accessibility and access to key services. The OSVI feeds into an agent-based model (ABM) that explores the capacity of the British Red Cross (BRC) to distribute relief during flood emergencies using strategies based upon the OSVI. A participatory modelling approach was utilised whereby the BRC were included in all aspects of the model development. The major contribution of this work is the novel synthesis of demographics analysis, vulnerability mapping and geospatial simulation. The project contributes to the growing understanding of vulnerability and response management within the NGO sector. It is hoped that the index and model produced will allow responder organisations to run simulations of similar emergency events and adjust strategic response plans accordingly

Virtual Clinical Trials : A tool for the Study of Transmission of Nosocomial Infections

A clinical trial is a study designed to demonstrate the efficacy and safety of a drug, procedure, medical device, or diagnostic test. Since clinical trials involve research in humans, they must be carefully designed and must comply strictly with a set of ethical conditions. Logistical disadvantages, ethical constraints, costs and high execution times could have a negative impact on the execution of the clinical trial. This article proposes the use of a simulation tool, the MRSA-T-Simulator, to design and perform "virtual clinical trials" for the purpose of studying MRSA contact transmission among hospitalized patients. The main advantage of the simulator is its flexibility when it comes to configuring the patient population, healthcare staff and the simulation environment

An Agent-Based Decision Support System for Hospitals Emergency Departments

AbstractModeling 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

Is Telehealth Better Used to Treat Patients or Help Other Physicians Treat Patients? An Agent-Based Modeling Study of Healthcare Provision

Telehealth, the delivery of medical care remotely, is hoped to increase access to specialty services and improve health care utilization. Physicians can provide telehealth to each other (e.g. specialist to generalist) or to patients. Specialists often treat complex patients who can be adequately cared for only in academic hospitals, suggesting that providing specialty services via telehealth will reallocate rather than reduce system utilization. Here I use agent-based modeling to simulate telehealth’s effects on clinical outcomes and system utilization in medical toxicology. I found that toxicologist-physician consultation increased patient health and decreased cost. Toxicologist-patient telehealth increased cost and system utilization but did not improve health. The effects were sensitive to patient complexity and the clinical efficacy of the toxicologist. Within the limitations of using simulated data and an incomplete model, these results suggest that telehealth is more cost-effective when used to provide toxicologist access to general physicians than to the public

Agent Based Model and Simulation of MRSA Transmission in Emergency Departments

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

Layout Evaluation by Simulation Protocol for Identifying Potential Inefficiencies Created by Medical Building Configuration

With the healthcare industry in a state of change, one focus is on efficiency in the healthcare environment. The trend for architects is a focus on an evidence-based design decision making process. In this context, simulation is gaining acceptance as a source of evidence. This research developed the Layout Evaluation by Simulation (LES) protocol to evaluate the design of a healthcare facility layout. The approach contains a Systems-of-Systems analysis for developing a healthcare delivery (HD) model, a computer model and simulation of an existing medical facility validated by existing data. Then simulations are run through the validated model inserting the future facility design to evaluate efficiency in a proposed new spatial layout. Through a real-world case study, the research contains an evaluation of the predictive capacity of the LES protocol. In the research, a completely Agent Based Modeling and Simulation, a completely Discrete Event Simulation, and a hybrid were investigated. As detail was added to all models, simulations were run creating a matrix of results for comparison to existing data. The LES protocol was confirmed to be effective. The results demonstrate that the healthcare delivery (HD) model provides a sufficient basis from which to develop the computer model and simulation. The LES protocol is a valuable tool for evaluating situations for emergent behavior. The research also confirmed the need for some degree of agent based modeling to detect emergent behavior

Agent based simulation to optimise emergency departments

Nowadays, many of the health care systems are large and complex environments and quite dynamic, specifically Emergency Departments, EDs. It is opened and working 24 hours per day throughout the year with limited resources, whereas it is overcrowded. Thus, is mandatory to simulate EDs to improve qualitatively and quantitatively their performance. This improvement can be achieved modelling and simulating EDs using Agent-Based Model, ABM and optimising many different staff scenarios. This work optimises the staff configuration of an ED. In order to do optimisation, objective functions to minimise or maximise have to be set. One of those objective functions is to find the best or optimum staff configuration that minimise patient waiting time. The staff configuration comprises: doctors, triage nurses, and admissions, the amount and sort of them. Staff configuration is a combinatorial problem, that can take a lot of time to be solved. HPC is used to run the experiments, and encouraging results were obtained. However, even with the basic ED used in this work the search space is very large, thus, when the problem size increases, it is going to need more resources of processing in order to obtain results in an acceptable time