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Dynamic Performance Assessment of Hospitals by Applying Credibility-Based Fuzzy Window Data Envelopment Analysis.

Pejman Peykani1, Elaheh Memar-Masjed2, Nasim Arabjazi3

  • 1School of Industrial Engineering, Iran University of Science and Technology, Tehran 1684613114, Iran.

Healthcare (Basel, Switzerland)
|May 28, 2022
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Summary

This study introduces a novel Credibility-based Fuzzy Window Data Envelopment Analysis (CFWDEA) for dynamic hospital performance evaluation. The CFWDEA method effectively handles ambiguous data and linguistic variables for accurate hospital ranking.

Keywords:
credibility theorydata envelopment analysisfuzzy optimizationhospital performance assessmentlinguistic variableswindow analysis

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Area of Science:

  • Healthcare Management
  • Operations Research
  • Data Analysis

Background:

  • Dynamic performance evaluation of hospitals is crucial for strategic decision-making.
  • Existing methods often struggle with data ambiguity and linguistic variables inherent in healthcare data.
  • Accurate hospital performance assessment requires robust methodologies that account for temporal changes and uncertainty.

Purpose of the Study:

  • To propose the Credibility-based Fuzzy Window Data Envelopment Analysis (CFWDEA) as a novel approach for dynamic hospital performance evaluation.
  • To address challenges posed by data ambiguity and linguistic variables in assessing hospital efficiency over time.
  • To demonstrate the applicability and efficacy of the CFWDEA method using real-world hospital data.

Main Methods:

  • Integration of Data Envelopment Analysis (DEA), window analysis, possibilistic programming, credibility theory, and chance-constrained programming (CCP).
  • Development of the CFWDEA model to incorporate fuzzy data and linguistic variables.
  • Application of the CFWDEA approach to a dataset of US hospitals for dynamic performance appraisal.

Main Results:

  • The proposed CFWDEA method successfully evaluates and ranks hospitals under conditions of fuzzy data and epistemic uncertainty.
  • Experimental results validate the usefulness of CFWDEA for dynamic performance appraisal, considering multiple inputs and outputs.
  • The approach effectively handles linguistic variables, enhancing the reliability of performance evaluations.

Conclusions:

  • The CFWDEA approach offers a robust and effective solution for dynamic hospital performance evaluation in complex data environments.
  • This methodology provides valuable insights for hospital management and policy-making by accounting for data ambiguity and uncertainty.
  • CFWDEA represents a significant advancement in applying advanced analytical techniques to healthcare performance assessment.