This paper explores the automation of healthcare process modeling using process mining and simulation for Chronic Obstructive Pulmonary Disease (COPD) clinical pathways. By integrating AnyLogic’s agent-based simulation with real-world hospitalization data, the study is able to validate process-mined models. This approach optimizes patient flow analysis and supports data-driven decision-making in healthcare process automation.
This paper illustrates an emergency response simulation for out-of-hospital cardiac arrest (OHCA) incidents using agent-based modeling and GIS integration. The authors applied AnyLogic simulation software to evaluate dispatch strategies and responder deployment, with the goal of improving survival rates and enhancing urban emergency response efficiency.
This study explores the role of healthcare resilience in emergency departments. An AnyLogic simulation model is used to assess cross-training strategies. The study identifies the most effective workforce flexibility policies to reduce patient waiting times and optimize resource allocation by simulating real-world emergency department operations. The findings highlight how cross-training nurses between triage, adult, and pediatric zones enhances healthcare resilience, ensuring efficient emergency care during patient demand surges.
This study explores the use of healthcare simulation in measuring and improving the resilience of emergency departments (EDs) during demand surges. The model uses AnyLogic discrete-event simulation to replicate patient flow, resource allocation, and response strategies to identify effective interventions. The research provides valuable insights for ED managers seeking to enhance operational efficiency and patient care under pressure.
A discrete event simulation model was developed for a network of eight major intensive care units (ICUs) as well as high-acuity units (HAUs) in British Columbia, Canada. The simulation model will be used to develop strategies for managing the combined impacts of COVID-19 and seasonal influenza without the need for extensive public health interventions to limit transmission.
The Covid-19 virus has substantially transformed many aspects of life, impacted industries, and revolutionized supply chains all over the world. System dynamics modeling can aid in predicting future outcomes of the pandemic and generate key learnings. This tutorial describes how the system dynamics simulation model was constructed for the Covid-19 pandemic using AnyLogic Software. The model serves as a general foundation for further epidemiological simulations and system dynamics modeling.
Physical distancing is recommended as the most efficient strategy for defending individuals from long-term global pandemics. It lowers the risk of community spreading, especially for indoor spaces. This paper aims to model physical-distancing behaviors in an educational facility, using agent-based simulation, and evaluate the impact of measures (e.g., controlling classroom capacity, breaktime scheduling) on physical distance violation risks.
In December 2019, there was a case of viral pneumonia in Wuhan. After confirming that the pathogen of this disease is a new coronavirus, the World Health Organization (WHO) confirmed and named it 2019-nCoV. The pneumonia caused by this pathogen infection is called a novel corona virus pneumonia.
To better understand the mode of transmission of 2019-nCoV among the population and the effects of control measures, the study was conducted using agent-based modeling (ABM) to simulate an interactive environment over a certain space-time range. The study simulates the trend of 2019-nCoV infection at different levels of close contact in order to provide relevant information and references.
Pertussis control remains a challenge due to recently observed effects of waning immunity to acellular vaccine and suboptimal vaccine coverage. Multiple outbreaks have been reported in different ages worldwide. For certain outbreaks, publichealth authorities can launch an outbreak response immunization (ORI) campaign to control pertussis spread. The authors developed an agent-based model to investigate effects of outbreak response immunization campaigns targeting young adolescents in averting pertussis cases. The experience proved that ABM offers a promising methodology to evaluate other public health interventions used in pertussis control. The authors also identified the strong need for further research into application of modeling to further our understanding of pertussis epidemiology.
Until relatively recently, developing hybrid simulation models using more than one simulation paradigm was a challenging task which required a degree of ingenuity on behalf of the modeler. Generally speaking, such hybrid models either had to be coded from scratch in a programming language, or developed using two (or more) different off-the-shelf software tools which had to communicate with each other through a user-written interface. Nowadays a number of simulation tools are available which aim to make this task easier. This paper does not set out to be a formal review of such software, but it discusses the increasing popularity of hybrid simulation and the rapidly developing market in hybrid modeling tools, focusing specifically on applications in health and social care and using experience from the Care Life Cycle project and elsewhere.