Nurse Staffing Management in the Context of Emergency Departments and Seasonal Respiratory Diseases: An Artificial Intelligence and Discrete-Event Simulation Approach
[EN] 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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::e5c7fe752d03762c7b2d1bc496b68aae |
| Acceso en línea: | https://riunet.upv.es/handle/10251/234920 |
| Access Level: | acceso embargado |
| Palabra clave: | Artificial intelligence (AI) Extreme gradient boosting (XGBoost) Discrete-event-simulation (DES) Emergency department (ED) Nurse staffing Seasonal respiratory diseases (SRDs) |
| Sumario: | [EN] 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. |
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