# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion  
The exponential growth of scientific publications in healthcare simulation reflects a broader trend in medical research where data-driven decision-making has become increasingly crucial. Similar to other rapidly evolving fields in healthcare, such as artificial intelligence and machine learning, the challenge lies not only in producing research but in effectively synthesizing and applying existing knowledge. This challenge is compounded by varying publication quality, accessibility issues, language barriers, and the constant need for current information. Our systematic review addresses these challenges by providing a comprehensive analysis of DES applications in healthcare, building upon previous reviews but offering novel insights into the statistical foundations of simulation models.  
Our analysis reveals that while DES software tools have evolved significantly since their introduction in the 1950s, their fundamental purpose remains constant across platforms. Arena's emergence as the most widely used software aligns with findings from previous industrial and healthcare simulation studies, suggesting a consolidation of preferred tools in the field. This trend parallels the evolution of other analytical tools in healthcare, where standardization has facilitated broader adoption and reliability.  
A significant finding of our review, distinguishing it from previous systematic reviews (see Figure 1 and Supplementary Materials Anne S1), is the critical role of statistical distributions in DES implementation. While prior reviews have focused on operational outcomes or specific healthcare applications, none have systematically examined the statistical underpinnings of these simulations. This gap is particularly noteworthy given that statistical distributions fundamentally influence model accuracy and, consequently, decision-making reliability.  
The absence of a universal DES model in healthcare applications, as revealed by our analysis, emphasizes the importance of context-specific modeling approaches. This finding aligns with previous healthcare modeling studies that highlight the uniqueness of each healthcare environment. Our review extends this understanding by demonstrating how sta  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Journal** & **Number of Publications (\%)** \\ \hline PharmacoEconomics & 21 (3.41) \\ Value in Health & 18 (2.92) \\ Journal of the Operational Research Society & 17 (2.76) \\ Health Care Management Science & 16 (2.60) \\ Medical Decision Making & 14 (2.27) \\ Operations Research for Health Care & 14 (2.27) \\ European Journal of Operational Research & 13 (2.11) \\ IISE Transactions on Healthcare Systems Engineering & 10 (1.62) \\ PLOS ONE & 10 (1.62) \\ Simulation Modelling Practice and Theory & 10 (1.62) \\ Others & 473 (76.80) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of publications by journal.  
tistical distribution selection must be tailored to specific healthcare contexts, often requiring direct input from healthcare workers through field interviews to ensure model accuracy.  
The diversity of DES software applications identified in our review (Arena, Simul8, AnyLogic, and ProModel) reflects the field's maturity and the varying needs of healthcare institutions. While these platforms share common objectives in modeling complex systems, their continued co-existence suggests that different healthcare contexts may benefit from different simulation approaches, a finding that has significant implications for future implementation strategies.