Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

Area of Science:

• Healthcare Management
• Operations Research
• Health Economics

Background:

• Hospital performance evaluation is vital for healthcare quality assessment and decision-making.
• Data uncertainty in performance metrics challenges accurate hospital efficiency measurement.
• Traditional Data Envelopment Analysis (DEA) models struggle with inherent data variability.

Purpose of the Study:

• To develop a novel Uncertain Common-Weights Data Envelopment Analysis (UCWDEA) model.
• To address limitations of traditional DEA in uncertain healthcare environments.
• To provide a robust and reliable method for hospital performance evaluation under uncertainty.

Main Methods:

• Incorporation of Uncertainty Theory (UT) to model uncertain input and output data.
• Utilization of a Common Set of Weights (CSW) technique for consistent comparison.
• Application of the UCWDEA model to a real-world dataset of 20 public hospitals in Tehran.

Main Results:

• The UCWDEA approach provides realistic efficiency scores, avoiding over-optimistic assessments.
• The model demonstrates linearity, enhancing its practical applicability.
• The study successfully assessed and ranked hospitals amidst uncertain conditions, showing improved discriminatory power and stability analysis.

Conclusions:

• The UCWDEA model offers valuable insights for policymakers regarding hospital performance under data uncertainty.
• Findings support optimizing resource allocation, performance benchmarking, and policy formulation in healthcare.
• The research enhances the overall efficiency and effectiveness of healthcare services through improved performance evaluation.