Optimizing Emergency Department Resource Allocation Using Discrete Event Simulation and Machine Learning Techniques

Abstract

Background: Optimizing resource allocation in emergency departments (ED) is challenging due to limited resources and high costs. Objectives: The objective of this study was to utilize data mining algorithms and simulation modeling to predict the length of stay (LOS) of patients and compare scenarios for increasing bed productivity. Methods: Data mining algorithms, including Random Forest (RF) regression and CatBoost (CB) regression models, were used to predict the LOS based on patient demographic information and vital signs. The process of admission to discharge in the ED was simulated, and different scenarios were compared to identify strategies for increasing bed productivity. Results: The combination of RF regression and CB regression models performed better than other methods in predicting the LOS of patients. Simulation modeling demonstrated that optimal resource allocation and increased bed productivity could be achieved using predicted LOS values. Conclusions: This study demonstrates that a combined approach of data mining and simulation can effectively manage ED resources and reduce congestion. The findings highlight the potential of advanced analytical techniques for improving healthcare service delivery and patient outcomes.