Compară metode

	Model de Ocupare a Patului de Spital	Model de predicție a readmisiilor spitalicești
Domeniu	Management sanitar	Management sanitar
Familie	Process / pipeline	Process / pipeline
Anul apariției	2000	1998
Autorul original	Healthcare operations researchers	Healthcare data analytics and outcomes research
Tip	Stochastic simulation and time-series forecasting	Logistic regression and machine learning methodology
Sursa seminală	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.
Denumiri alternative	Bed Occupancy Forecasting, Hospital Census Prediction	Readmission Risk Prediction, Hospital Readmission Forecasting
Înrudite	5	5
Rezumat	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.