# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
###### Abstract  
The healthcare sector, as a complex industrial system, faces significant challenges in delivering quality care amid resource constraints, driving the increased adoption of discrete event simulation (DES) as a tool for enhancing operational efficiency. While DES has proven valuable in healthcare operations, there is limited understanding of the statistical distributions employed in its implementation and its technological evolution. This study conducts an innovative review examining the diffusion of DES in health services, analyzing both the statistical distributions used in medical services simulation and the advancement of DES technologies. Through a comprehensive analysis of 616 publications from 2010 to 2022, we investigated DES utilization patterns and technological evolution and conducted a comparative analysis between pre- and post-COVID-19 pandemic periods, evaluating publication trends by country. The results reveal a significant increase in DES publications, an expansion of journals publishing DES-related articles, and notable technological advancements in simulation capabilities, particularly following the COVID-19 pandemic. These findings demonstrate the growing relevance of DES in healthcare research and its crucial role in process automation and decision-making within the industrial healthcare environment.  
literature review; simulation; healthcare; hospitals; patients; DES 2020  
4 February 20256 February 20  
makes it especially valuable for healthcare management. Multiple software tools support these applications, including ARENA, Simuls, AnyLogic, ProModel, and FlexSim, enabling researchers and practitioners to model complex healthcare systems effectively.  
The technological evolution of DES has paralleled the advancement of Industry 4.0, incorporating emerging technologies such as artificial intelligence, Internet of Things (IoT), and cloud computing [15]. These technological integrations have enhanced DES capabilities in healthcare settings, enabling more accurate modeling, real-time decision support, and improved stakeholder engagement [16].  
Despite the extensive literature on DES applications in healthcare, a significant gap exists in understanding the statistical distributions employed in these simulations. While previous reviews have examined various aspects of DES implementation, none have comprehensively analyzed the statistical distributions used in healthcare case studies. This analysis is crucial as these distributions fundamentally affect the accuracy of real-life situation modeling and subsequent decision-making quality.  
This study presents a systematic review of DES applications in healthcare between 2010 and 2022, examining publication patterns by country, implementation across medical services, software usage, and achieved improvements. Importantly, we provide the first comprehensive analysis of statistical distributions utilized in healthcare DES implementations. The paper is structured as follows: Section 2 presents the methodology following PRISMA guidelines; Section 3 provides a comprehensive analysis of publication trends and patterns; Section 4 presents an in-depth discussion covering software analysis, application areas, implementation improvements, statistical distributions, technological evolution, and implementation implications; and Section 5 presents our conclusions and recommendations for future research.