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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Efficacité hospitalière par DEA× | Modèle de prédiction de réadmission hospitalière× | |
|---|---|---|
| Domaine | Gestion des soins de santé | Gestion des soins de santé |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1978 | 1998 |
| Auteur d'origine≠ | Abraham Charnes, William Cooper, Edward Rhodes | Healthcare data analytics and outcomes research |
| Type≠ | Non-parametric frontier estimation technique | Logistic regression and machine learning methodology |
| Source fondatrice≠ | 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 ↗ | 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 ↗ |
| Alias | Hospital DEA, Healthcare DEA | Readmission Risk Prediction, Hospital Readmission Forecasting |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | 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. |
| ScholarGateJeu de données ↗ |
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