Public Hospitals Performance Measurement through a Three-Staged Data Envelopment Analysis Approach: Evidence from an Emerging Economy

Seyed Hossein Razavi Hajiagha, Hannan Amoozad Mahdiraji*, Shide Sadat Hashemi, Jose Arturo Garza-Reyes, Rohit Joshi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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This study proposes a three-staged approach to data envelopment analysis (DEA) modeling for hospital efficiency. The approach aims to overcome the constraint on the number of inputs/outputs relative to the number of DMUs. Initially, the principal components of all inputs and outputs are determined using principal component analysis (PCA). Next, these principal components enter a factor analysis (FA) process to construct a two-level hierarchy of inputs/outputs and to establish a weighting scheme based on explained variances of components. Finally, a two-level DEA (TLDEA) method is applied to the resultant framework to determine the relative efficiency of hospitals using data from the healthcare context of Iran as an emerging economy. The outcomes of applying the proposed PCA-FA-TLDEA approach are argued to offer a substantial increase in the discriminatory power of classical DEA methods and could incorporate a relatively large set of inputs/outputs already existing in the hospital efficiency literature. As demonstrated in the evaluated hospitals, the PCA-FA-TLDEA methodology improved the discrimination from 0% in the original DEA to 45%. The paper proposes a novel three-stage DEA model by using PCA to extract the principal components from the inputs and outputs; therefore, reducing the number of inputs and outputs and their inter-correlations. Secondly, a hierarchy of inputs and outputs by applying FA to the principal components is constructed. Finally, the TLDEA method to the hierarchy of inputs and outputs is applied to evaluate the performance of public hospitals.
Original languageEnglish
Pages (from-to)1-26
Number of pages26
JournalCybernetics and Systems
Issue number1
Early online date26 Mar 2022
Publication statusPublished - 2 Jan 2023


  • Discrimination power
  • efficiency
  • factor analysis
  • hospital
  • principal component analysis
  • two-level data envelopment analysis


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