Linganisha mbinu

	Modeli wa Ut napilika wa Kulazwa Hospitalini	Ufanisi wa Hospitali wa DEA
Nyanja	Usimamizi wa Huduma za Afya	Usimamizi wa Huduma za Afya
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1998	1978
Mwanzilishi	Healthcare data analytics and outcomes research	Abraham Charnes, William Cooper, Edward Rhodes
Aina	Logistic regression and machine learning methodology	Non-parametric frontier estimation technique
Chanzo asilia	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.	Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
Majina mbadala	Readmission Risk Prediction, Hospital Readmission Forecasting	Hospital DEA, Healthcare DEA
Zinazohusiana	5	5
Muhtasari	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.	Data Envelopment Analysis (DEA) is a linear programming technique for measuring the relative efficiency of multiple hospitals using multiple inputs and outputs. Introduced by Charnes, Cooper, and Rhodes in 1978, DEA has become the standard method for benchmarking hospital performance in healthcare systems worldwide.