Post-earthquake emergency nurse allocation: a human resource management approach based on simulation modeling

This study aims to develop a mathematical model to estimate the number of emergency nurses required to ensure the continuity of healthcare services in the aftermath of an earthquake. The February 6, 2023, Kahramanmaraş earthquake, which severely impacted southeastern Turkey, caused widespread devastation in Hatay province. The destruction of infrastructure, along with the large number of casualties and injuries, created an overwhelming demand for medical services, putting an extraordinary strain on the region’s healthcare system. Hatay, with limited medical resources and a single operating hospital, faced significant challenges in maintaining adequate care for the injured. This situation underscores the critical need for effective planning and resource allocation, particularly in estimating the number of emergency nurses required to manage the immediate aftermath of such disasters. The study focused on Hatay Mustafa Kemal University Hospital, the sole healthcare provider in Hatay, following the 2023 Kahramanmaraş earthquakes. Using real field data (e.g., building collapse rates, injury estimations, and population distribution), a simulation was conducted. The M/M/s queuing theory model was applied to calculate the number of nurses required, factoring in patient arrival rates, nurse care capacity, and working shifts. Based on an estimated 11,645 injured patients over 144 h, the model concluded that 27 nurses per shift (totaling 81 nurses for 24-hour care) would be necessary to sustain full-capacity service. The projections closely aligned with actual hospital data. The model provides a scalable, scenario-sensitive planning tool that can support emergency preparedness efforts. Its basis in real disaster data enhances both its reliability and applicability, positioning it as a practical decision-making aid for policymakers and hospital administrators aiming to strengthen disaster organizational resilience through evidence-based nurse staffing strategies.