Nurse Staffing Management in the Context of Emergency Departments and Seasonal Respiratory Diseases: An Artificial Intelligence and Discrete-Event Simulation Approach. - Ortiz-Barrios, Miguel; Cuenca, Llanos; Arias-Fonseca, Sebastián; McClean, Sally; Pérez-Aguilar, Armando (Journal of Medical Systems, 2025)
Emergency Departments (EDs) usually experience nursing shortages during Seasonal Respiratory Diseases (SRDs). As a result, patient waiting times for medical treatment increase with the consequent overcrowding, high intra-hospital infection rates, and no-shows. Therefore, the nurse staffing must be balanced with the projected volume of SRD-related ED admissions to EDs. In this article, we propose merging Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to build remedies that diminish the waiting times for nursing care in mild and severe respiratory-affected patients. We first implemented Extreme Gradient Boosting (XGBoost) to calculate the probability of treatment within the ED wards. Afterwards, we plugged the XGBoost predictions into a simulation model to evaluate whether the current nurse staff was sufficient to ensure the timely treatment of the expected respiratory-affected patients. Ultimately, we pretested three improvement scenarios recommended by the hospital administrators to tackle the imbalance problem. A Spanish ED was involved in the project to validate the suggested approach. The specificity of the predictive AI-based model was 95.97% (CI 95% 93.07% − 97.90%), while the specificity was 82.0% (CI 95% 73.05% − 88.96%). On a different tack, the positive and negative predictive scores corresponded to 87.23% (CI 95% 78.76% − 93.22%) and 94.08% (95% CI 90.80% − 96.45%). Furthermore, the Area Under Receiver Operator Characteristic (AU-ROC) curve was 89.00% (CI 95% 84.46% − 94.78%). Ultimately, the median waiting time for respiratory support use was lessened between 0.88 and 7.51 h after using a new nurse staffing configuration.