Evaluating the Impact of COVID-19 on Emergency Department Operations: A Combined Agent-Based and Discrete Event Simulation Model with Anomaly Detection

The COVID-19 pandemic has significantly impacted healthcare systems, particularly altering the operations of emergency departments (EDs). This study evaluates ED operational efficiency during and after the pandemic using a hybrid approach that combines Agent-Based Simulation (ABS) and Discrete Event Simulation (DES), integrated with an anomaly detection system. The simulations provide a comprehensive analysis of patient flow, resource utilization, and treatment processes, while the anomaly detection system identifies unusual operational patterns and potential crises in real-time. Key performance metrics, including average dwelling time, wait times, treatment times, and staff utilization, were analyzed. Results show substantial differences between the pandemic and post-pandemic periods: during the pandemic, the ABS model recorded an average dwelling time of 44.3 minutes with 60% staff utilization, while the DES model showed 122.2 minutes with 110.1% utilization. Post-pandemic, the ABS model improved to a dwelling time of 24.5 minutes and 70% staff utilization, and the DES showed reductions to 69.2 minutes and 49.5% utilization. Moreover, integrating anomaly detection further enhanced the ED's ability to manage operational disruptions proactively, reducing response times and improving overall efficiency. This study underscores the importance of combining simulation and anomaly detection to enhance ED preparedness and resilience for future healthcare crises.