Introduction
The need for healthcare resilience has become more critical, especially in light of global health crises such as the COVID-19 pandemic. Emergency departments (EDs) face increasing challenges in managing patient demand surges, which lead to overcrowding, long waiting times, and strained resources. One of the key strategies to improve ED resilience is workforce flexibility, specifically through cross-training nurses. This will allow resource reallocation in response to sudden fluctuations in patient demand.
The researchers developed a discrete-event simulation model, using AnyLogic healthcare simulation software, to evaluate different cross-training policies in an ED in Abu Dhabi, UAE. The simulation assesses how cross-trained nurses from different departments can enhance operational efficiency and reduce patient length of stay, strengthening healthcare resilience during demand surges.
Simulation model
The simulation model was validated in a previous study, ensuring its validity in replicating the operations of the actual emergency department within the hospital.
The AnyLogic-based discrete-event simulation model replicates real-world ED operations, including patient flow, resource allocation, and service capacity. The model integrates triage, adult, pediatrics, and fast-track zones, each with distinct patient categories and staffing levels. It also incorporates variability in patient arrivals, resource availability, and treatment durations. The model allows the evaluation of different cross-training strategies to improve healthcare resilience.
The simulation explores two cross-training policies: (1) pooling nurses between the triage and adult zones and (2) expanding cross-training to include nurses from the pediatrics section. The impact of these policies is measured through key performance indicators such as patient length of stay, nurse utilization rates, and system resilience under surge conditions.
Read also: Explore additional resources on healthcare simulation.
Results
The simulation revealed that without cross-training, patient length of stay in the triage room increased by 622.3% during demand surges, indicating severe congestion. Implementing the first cross-training policy (triage-adult zone pooling) reduced triage wait times by 91.71% and decreased adult zone stay by 24.27%. Expanding the cross-training to the pediatrics section further reduced patient wait times across all areas. This strategy demonstrates greater flexibility in handling surges and enhancing healthcare resilience.
However, the study also identified limitations at extreme demand levels. Under an intense 100-patient surge scenario, the benefits of cross-training diminished. This suggests the need for additional strategies such as dynamic staffing adjustments and real-time decision support systems to further improve healthcare resilience. Future research will explore adaptive cross-training models that dynamically reallocate staff based on real-time patient inflows.