विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| डीईए अस्पताल दक्षता× | अस्पताल में पुनः भर्ती भविष्यवाणी मॉडल× | |
|---|---|---|
| क्षेत्र | स्वास्थ्य सेवा प्रबंधन | स्वास्थ्य सेवा प्रबंधन |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1978 | 1998 |
| प्रवर्तक≠ | Abraham Charnes, William Cooper, Edward Rhodes | Healthcare data analytics and outcomes research |
| प्रकार≠ | Non-parametric frontier estimation technique | Logistic regression and machine learning methodology |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम | Hospital DEA, Healthcare DEA | Readmission Risk Prediction, Hospital Readmission Forecasting |
| संबंधित | 5 | 5 |
| सारांश≠ | 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. |
| ScholarGateडेटासेट ↗ |
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