Discrete Event Simulation-Based Analysis and Optimization of Emergency Patient Scheduling Strategies

Background: In the era of Health 4.0, Emergency Departments (EDs) face increasing crowding and complexity, necessitating smart management solutions to balance efficiency with equitable care. Effective scheduling is critical for optimizing patient throughput and mitigating congestion. Methods: This paper constructs a decision support framework using Discrete Event Simulation (DES) to evaluate three patient scheduling strategies, including the Initial-First policy, Alternating 1:1 policy and a Slack-Based dynamic policy. The simulation framework has been conducted using a standardized operational dataset representing typical ED dynamics. The threshold of SBP was optimized by a grid search method to guarantee an objective comparison. Results: The simulation results show that when adopting the optimized SBP policy, the mean waiting time was shortened by around 23.8%, thus meeting all triage service level targets. Also, it could be seen that Slack-Based dynamic policy was robust under different arrival rates and physician staffing levels. Conclusions: This proposed model can provide a real-time and dynamic solution for ED resource allocation, meeting the demand of modern smart hospitals management.