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Annals of Operations Research

, Volume 271, Issue 2, pp 679–699 | Cite as

Combining principal component analysis and the evidential reasoning approach for healthcare quality assessment

  • Guilan Kong
  • Lili Jiang
  • Xiaofeng Yin
  • Tianbing Wang
  • Dong-Ling Xu
  • Jian-Bo Yang
  • Yonghua Hu
Original Research

Abstract

Patient experience and satisfaction surveys have been adopted worldwide to evaluate healthcare quality. Nevertheless, national governments and the general public continue to search for optimal methods to assess healthcare quality from the patient’s perspective. This study proposes a new hybrid method, which combines principal component analysis (PCA) and the evidential reasoning (ER) approach, for assessing patient satisfaction. PCA is utilized to transform correlated items into a few uncorrelated principal components (PCs). Then, the ER approach is employed to aggregate extracted PCs, which are considered as multiple attributes or criteria within the ER framework. To compare the performance of the proposed method with that of another assessment method, analytic hierarchy process (AHP) is employed to acquire the weight of each assessment item in the hierarchical assessment framework, and the ER approach is used to aggregate patient evaluation for each item. Compared with the combined AHP and ER approach, which relies on the respondents’ subjective judgments to calculate criterion and subcriterion weights in the assessment framework, the proposed method is highly objective and completely based on survey data. This study contributes a novel and innovative hybrid method that can help hospital administrators obtain an objective and aggregated healthcare quality assessment based on patient experience.

Keywords

Healthcare quality assessment Patient experience and satisfaction Principal component analysis Analytic hierarchy process The evidential reasoning approach 

Notes

Acknowledgements

This study was supported by Grants from the National Natural Science Foundation of China (Grant Nos. 81771938, 81301296). This study was also supported by Grants from Peking University (Grant Nos. PKU2017LCX05, BMU20160592).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Guilan Kong
    • 1
  • Lili Jiang
    • 2
  • Xiaofeng Yin
    • 3
  • Tianbing Wang
    • 3
  • Dong-Ling Xu
    • 4
  • Jian-Bo Yang
    • 4
  • Yonghua Hu
    • 1
    • 5
  1. 1.Medical Informatics CenterPeking UniversityBeijingChina
  2. 2.Taikang Life Insurance Co. LtdBeijingChina
  3. 3.Department of Trauma and OrthopaedicsPeking University People’s HospitalBeijingChina
  4. 4.Decision and Cognitive Sciences Research CentreThe University of ManchesterManchesterUK
  5. 5.School of Public HealthPeking UniversityBeijingChina

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