Simulation of Overcrowding in Emergency Departments


Emergency department (ED) overcrowding is a problem in many parts of the world, including the United States. Patient satisfaction can reflect the quality of services in emergency departments (Salway et al., 2017). The overcrowding of the emergency department is usually accompanied by negative errors and consequences in the system of his care. Ultimately, emergency department overcrowding comes from hospitals, so the solutions to this complex problem lie within the emergency department itself and beyond.

Study Type

A case study by Penga et al. is a discrete-event simulation model for evaluating and reducing the flow of emergency patients. Simulation models are widely used to test various ‘what if’ scenarios with minimal time and expense, which has made simulation a widespread ED improvement tool (Peng et al., 2020). Various surgical options were tested using a validated model to reduce emergency department overcrowding and patient waiting times.

Purpose of the Study

This study proposes a physician in triage (PIT) strategy to improve emergency department overcrowding. Solutions are sought using PIT to identify areas for improvement in ER patient flow based on a validated simulation model. Using the proposed simulation model to study the operation of the PIT, the difference in ED performance is measured. The article proposes and tests various scenarios for improving ED, including both resource utilization and process optimization.


As the demand for health care continues to grow, the importance of reducing costs, increasing efficiency, and delivering quality services is growing. This study used a quantitative approach for hospital decision-makers to explore the multiple trade-offs between efficiency, workload, and throughput. The Acute Care Hospital provides medical services for community outreach programs, outpatient care programs, and inpatient services with 554 beds and 78 nursing cradles (Peng et al., 2020). Thus, in order to create a realistic picture of department overcrowding, the researchers created a simulation model with a sample size of 554. Key modeling steps included problem setting, data collection, and model transformation, however no specific time frame was given in the study.

Model performance is validated using current ED flow metrics and used to quantitatively test multiple design options across ED operations to determine the most efficient staffing configurations. Several strategies have been proposed to improve patient flow in the emergency room (ER), such as changing the ER plan, changing work processes, and adjusting resources (Peng et al., 2020). Using simulations, modeling, and analysis of emergency care systems have shown that physician triage generally results in longer patient stays. The reduction in patient waiting time for a doctor’s diagnosis is likely due to the addition of PIT.

Data Analysis and Study Reliability

The analysis of ED is a complex task since the processes of ED are characterized by high dynamism, complexity, and interdisciplinary nature. ED works 24 hours a day, 7 days a week, so it is not possible to interrupt the system of ED operations to evaluate their effectiveness (Yousefi & Yousefi, 2019). Compared to other methods used in ED, simulation modeling is much more flexible and versatile, allowing you to freely make assumptions about input data such as arrival patterns and service time.

The results show that a simulated implementation of PIT can reduce the length of stay (LOS) of an ER patient by an average of 34% and the waiting time for an appointment (WTBS) by 49% in all scenarios studied. The optimal scenario reduces LOS by 39% when PIT is administered (Peng et al., 2020). The proposed method can be applied to improve the efficiency of healthcare systems in the context of the current COVID-19 pandemic. The results can be considered reliable for the ED since the researchers fully analyzed the department’s process when creating the simulator. The data shows the number of patients arriving per hour in a day, medical operation procedures after referral to a treatment room, and more.

No adverse events occurred during the study although required additional interventions in the script. Initially, it was decided to introduce only one forced marshaling yard PIT open for operation 24 hours a day (Peng et al., 2020). According to PIT testing results, opening just one individual PIT for patients may have an increased risk of crowding patients in the waiting room, leading to even more. More efficient and effective is the intervention of PIT-1 and PIT-2, which expands on the original scenario. However, in terms of the evidence provided in the article as well as the results, the study can be considered robust.

Previous Research and Implications for Clinical Practice

Emergency room overcrowding is also a major global problem. Some researchers have tried to change triage protocols to smooth out the flow of patients to ED (Peng et al., 2020). These various interventions have some potential to improve the emergency department’s performance. For example, adding more triage stations similar to PITs did not significantly improve throughput time in a past pilot test. However, according to the results, the developed model with the addition of two or more PITs will help cope with excessive overcrowding in emergency departments.


The emergency department is the only department that faces overcrowding and congestion of patients, indicating the closeness of the hospital’s internal policy to the national health policy. Overcrowding in emergency departments (ED) creates problems for patients and staff, including increased medical errors, patient deaths, and financial loss to hospitals. Penga et al. propose a physician in triage (PIT) strategy, which they tested with a simulation model. The results of the study showed that the strategy is effective and can be implemented in emergency departments.


Peng, Q., Yang, J., Strome, T., Weldon, E., & Chochinov, A. (2020). Evaluation of physician in triage impact on overcrowding in emergency department using discrete-event simulation. Journal of Project Management, 211–226. Web.

Salway, R. J., Valenzuela, R., Shoenberger, J. M., Mallon, W. K., & Viccellio, A. (2017). Emergency department (ED) overcrowding: Evidence-based answers to frequently asked questions. Revista Médica Clínica Las Condes, 28(2), 213–219. Web.

Yousefi, M., & Yousefi, M. (2019). Human resource allocation in an emergency department. Kybernetes, 49(3), 779–796. Web.

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NursingBird. "Simulation of Overcrowding in Emergency Departments." March 15, 2023.