Nurse Staffing Management in the Context of Emergency Departments and Seasonal Respiratory Diseases: An Artificial Intelligence and Discrete-Event Simulation Approach.
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 b...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 20 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
8/16/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187385010&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187385010 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 8/16/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187385010 187385010 187385010 10.1007/s10916-025-02242-8 187385010 ppf: 1 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Nurse Staffing Management in the Context of Emergency Departments and Seasonal Respiratory Diseases: An Artificial Intelligence and Discrete-Event Simulation Approach. aug: au: Ortiz-Barrios, Miguel Cuenca, Llanos Arias-Fonseca, Sebastián McClean, Sally Pérez-Aguilar, Armando affil: https://ror.org/01460j859 Centro de Investigación en Gestión e Ingeniería de Producción (CIGIP), Universitat Politècnica de València, Camino de Vera, s/n, 46022, Valencia, Valencia, Spain sug: subj: Respiratory Tract Infections Nursing Nursing Staff, Hospital Labor Supply Personnel Staffing and Scheduling Evaluation Artificial Intelligence Simulations Prediction Models Emergency Service Human Boosting Machine Learning Algorithms Health Facility Administrators Spain Sensitivity and Specificity Predictive Validity Confidence Intervals Descriptive Statistics ROC Curve Waiting Lists Funding Source Analytic Research Analysis of Variance Male Female Adult Middle Age Length of Stay Respiratory Therapy Chi Square Test Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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