Comparer des méthodes

	Modèle de prédiction de réadmission hospitalière	Simulation de flux de patients
Domaine	Gestion des soins de santé	Gestion des soins de santé
Famille	Process / pipeline	Process / pipeline
Année d'origine	1998	1990
Auteur d'origine	Healthcare data analytics and outcomes research	Operations research and management science
Type	Logistic regression and machine learning methodology	Discrete event simulation technique
Source fondatrice	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.	Pidd, M. (1992). Computer Simulation in Management Science (3rd ed.). John Wiley & Sons. ISBN: 9780471939314
Alias	Readmission Risk Prediction, Hospital Readmission Forecasting	Healthcare DES, Patient Movement Simulation
Apparentées	5	5
Résumé	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.	Discrete Event Simulation (DES) is a computational technique that models the movement of patients through healthcare facilities by simulating individual patient journeys and interactions with resources (staff, beds, equipment). DES allows realistic representation of complex, stochastic healthcare processes and supports 'what-if' analysis without disrupting live operations.