手法を比較

	再入院予測モデル	病院ベッド稼働率モデル
分野	医療経営学	医療経営学
系統	Process / pipeline	Process / pipeline
提唱年	1998	2000
提唱者	Healthcare data analytics and outcomes research	Healthcare operations researchers
種類	Logistic regression and machine learning methodology	Stochastic simulation and time-series forecasting
原典	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.	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.
別名	Readmission Risk Prediction, Hospital Readmission Forecasting	Bed Occupancy Forecasting, Hospital Census Prediction
関連	5	5
概要	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.	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.