Võrdle meetodeid

	Haigla korduvhospitaliseerimise ennustusmudel	Personali Suhtarvu Analüüs
Valdkond	Tervishoiukorraldus	Tervishoiukorraldus
Perekond	Process / pipeline	Process / pipeline
Tekkeaasta	1998	1990
Looja	Healthcare data analytics and outcomes research	Healthcare operations and nursing research
Tüüp	Logistic regression and machine learning methodology	Quantitative workforce planning methodology
Algallikas	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.	Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA, 288(16), 1987–1993.
Rööpnimetused	Readmission Risk Prediction, Hospital Readmission Forecasting	Staffing Model, Nursing Ratio Analysis
Seotud	5	5
Kokkuvõte	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.	Staffing Ratio Analysis is a systematic method for determining appropriate healthcare worker levels (nurses, physicians, technicians) based on patient volume, acuity, and task requirements. Research shows that staffing levels directly impact patient safety, quality, and staff burnout; systematic analysis supports evidence-based workforce planning.