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| Modèle de prédiction de réadmission hospitalière× | Efficacité hospitalière par DEA× | |
|---|---|---|
| Domaine | Gestion des soins de santé | Gestion des soins de santé |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1998 | 1978 |
| Auteur d'origine≠ | Healthcare data analytics and outcomes research | Abraham Charnes, William Cooper, Edward Rhodes |
| Type≠ | Logistic regression and machine learning methodology | Non-parametric frontier estimation technique |
| Source fondatrice≠ | 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 ↗ | Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. DOI ↗ |
| Alias | Readmission Risk Prediction, Hospital Readmission Forecasting | Hospital DEA, Healthcare DEA |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. |
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