Optimizing Emergency Room Patient Flow: A Simulaiton-Based Approach to Reducing Wait Times

Abstract

The COVID-19 pandemic caused abrupt changes in emergency care, highlighting the need for rapid response and efficient resource use. This study, conducted in the emergency department of a private hospital with over 112,000 arrivals in 2023, noted a post-pandemic increase in ER visits surpassing pre-pandemic levels.Employing the DMAIC (Define, Measure, Analyze, Improve, Control) methodology and the Critical Incident Approach, we identified key issues in patient experience. Due to the non-linear nature of patient flow, we developed an agent-based simulation, contrasting with the traditional discrete simulations commonly used in ER settings. Our simulation treated patients as semi-dependent entities and more accurately modeled the movements and actions of medical staff.The simulation predicted a 35% reduction in waiting times by implementing a low-complexity patient flow, which was then put into practice. Timeliness showed the largest gap between importance and satisfaction. Actual implementation exceeded predictions, achieving a 50% reduction in waiting times and a 33% reduction in total patient time. We focused on objective metrics over patient satisfaction surveys.This study demonstrates the value of agent-based simulations in modeling complex, variable processes like emergency rooms, despite potential discrepancies between models and reality. By accurately simulating patient and staff interactions, we could implement a differentiated flow for low-complexity patients—nearly half of ER visits—significantly improving efficiency.We plan to share this methodology with public hospitals and promote the Critical Incident Approach to enhance patient satisfaction. Future work will refine simulations to better mirror real conditions, suggesting potential applications in other healthcare settings.