Comparer des méthodes

	Modèles d'occupation des lits d'hôpital	Modèle de prédiction de réadmission hospitalière
Domaine	Gestion des soins de santé	Gestion des soins de santé
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000	1998
Auteur d'origine	Healthcare operations researchers	Healthcare data analytics and outcomes research
Type	Stochastic simulation and time-series forecasting	Logistic regression and machine learning methodology
Source fondatrice	Tikk, D., Kóczy, L. T., & Gedeon, T. D. (2003). A survey on fuzzy relational equations and their applications in web intelligence. In W. Pedrycz (Ed.), Handbook of Granular Computing (pp. 521–542). John Wiley & Sons.	Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418–1428.
Alias	Bed Occupancy Forecasting, Hospital Census Prediction	Readmission Risk Prediction, Hospital Readmission Forecasting
Apparentées	5	5
Résumé	Hospital bed occupancy models forecast the number of occupied beds at future times by analyzing admission patterns, length of stay distributions, and discharge dynamics. These models support tactical decisions about staffing, supply chain management, and strategic decisions about capacity expansion.	Hospital readmission prediction models use statistical and machine learning techniques to identify patients at high risk of returning to the hospital shortly after discharge. These models guide targeted discharge planning and follow-up to improve outcomes and reduce costs.