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| Model til forudsigelse af genindlæggelse på hospital× | Analyse af personalenormering× | |
|---|---|---|
| Fagområde | Sundhedsledelse | Sundhedsledelse |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1998 | 1990 |
| Ophavsperson≠ | Healthcare data analytics and outcomes research | Healthcare operations and nursing research |
| Type≠ | Logistic regression and machine learning methodology | Quantitative workforce planning methodology |
| Oprindelig kilde≠ | 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. DOI ↗ | 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. DOI ↗ |
| Aliasser | Readmission Risk Prediction, Hospital Readmission Forecasting | Staffing Model, Nursing Ratio Analysis |
| Relaterede | 5 | 5 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
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