# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 5 Conclusions  
This systematic review of 616 publications, including 349 case studies, reveals significant patterns in the implementation and evolution of discrete event simulation (DES) in healthcare settings between 2010 and 2022. Our analysis yielded three key findings:  
First, the application of DES in healthcare demonstrates distinct evolutionary phases, progressing from basic process modeling to sophisticated, technology-integrated solutions. This evolution is particularly evident in the increasing complexity of models and their integration with hospital information systems. The COVID-19 pandemic accelerated this evolution, spurring rapid adoption of advanced modeling approaches and real-time optimization capabilities. Second, our analysis of statistical distributions in DES implementations reveals context-specific patterns in distribution selection. While certain distributions show a better fit for particular applications (e.g., Poisson for ED arrivals, lognormal for length of stay), successful implementation requires careful validation within each specific healthcare context. This finding emphasizes the importance of rigorous statistical testing and data validation in DES model development. Third, the geographical analysis shows a concentration of DES applications in developed healthcare systems, particularly in the USA, UK, and Canada, while revealing an increasing trend of adoption in emerging healthcare markets. This pattern suggests both the maturity of DES applications in established systems and growing recognition of its value in developing healthcare environments. Given these findings, and acknowledging the challenges identified in our review, we propose four key implementation strategies to enhance DES adoption and effectiveness in healthcare organizations:  
1. Implementation Approach (addressing evolutionary phases)
2. Begin with well-defined, high-impact departments where data collection is feasible. 3. Establish clear success metrics before implementation. 4. Develop pilot projects before full-scale deployment. 5. Ensure stakeholder engagement throughout the process. 6. Model Development (addressing statistical validation needs)
7. Select appropriate statistical distributions based on empirical data analysis. 8. Validate models using multiple approaches (statistical testing, stakeholder verification). 9. Document all assumptions and limitations clearly. 10. Plan for regular model updates and refinements. 3. Technical Integration (addressing technological evolution) * Assess existing IT infrastructure capabilities before implementation. * Implement data collection processes early in the project. * Ensure compatibility with hospital information systems. * Plan for scalability in future expansions. * Organizational Considerations (addressing adoption patterns) * Invest in staff training and development. * Establish clear communication channels among stakeholders. * Create standard operating procedures for model maintenance. * Monitor and document improvements systematically. However, several significant challenges remain in healthcare DES implementation:
* Interoperability: Integration with diverse hospital information systems remains complex. * Data Quality: Ensuring consistent, high-quality data collection across departments. * Organizational Resistance: Overcoming resistance to change and new technological adoption. * Resource Constraints: Addressing implementation costs and training requirements. * Standardization: Lack of standardized validation methodologies across healthcare settings. * Scalability: Challenges in scaling successful pilot projects to full implementation. Future research should focus on addressing these challenges while expanding DES applications in resource-limited settings and emerging healthcare markets. Particular attention should be paid to developing standardized validation methodologies, improving system integration capabilities, and creating frameworks for sustainable implementation in diverse healthcare environments.