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


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.


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



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).


  1. Behara, R. S., Fisher, W. W., & Lemmink, J. G. A. M. (2002). Modelling and evaluating service quality measurement using neural networks. International Journal of Operations and Production Management, 22, 1162–1185.CrossRefGoogle Scholar
  2. Büyüközkan, G., & Çifçi, G. (2012). A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Systems with Applications, 39, 2341–2354.CrossRefGoogle Scholar
  3. Büyüközkan, G., Çifçi, G., & Güleryüz, S. (2011). Strategic analysis of healthcare service quality using fuzzy AHP methodology. Expert Systems with Applications, 38, 9407–9424.CrossRefGoogle Scholar
  4. Carlucci, D., Renna, P., & Schiuma, G. (2013). Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network. Health Care Manage Science, 16, 37–44.CrossRefGoogle Scholar
  5. Department of Health. (2000). The NHS plan. London: The Stationery Office.Google Scholar
  6. Department of Health. (2013). Victorian health service performance monitoring framework. Victoria: Victorian Government.Google Scholar
  7. Fragkiadakis, G., Doumpos, M., Zopounidis, C., & Germain, C. (2016). Operational and economic efficiency analysis of public hospitals in Greece. Annals of Operations Research, 247, 787–806.CrossRefGoogle Scholar
  8. Goldstein, E., Farquhar, M., Crofton, C., Darby, C., & Garfinkel, S. (2005). Measuring hospital care from the patients’ perspective: An overview of the CAHPS Hospital Survey development process. Health Services Research, 40, 1977–1995.CrossRefGoogle Scholar
  9. Harris, L. E., Swindle, R. W., Mungai, S. M., Weinberger, M., & Tierney, W. M. (1999). Measuring patient satisfaction for quality improvement. Medical Care, 37, 1207–1213.CrossRefGoogle Scholar
  10. Ishizaka, A., Balkenbourg, D., & Kaplan, T. (2010). Does AHP help us to make a choice? An experimental evaluation. Journal of the Operational Research Society, 62, 1801–1812.CrossRefGoogle Scholar
  11. Jenkinson, C., Coulter, A., & Bruster, S. (2002). The Picker Patient Experience Questionnaire: Development and validation using data from in-patient surveys in five countries. International Journal for Quality in Health Care, 14, 353–358.CrossRefGoogle Scholar
  12. Jenkinson, C., Coulter, A., Reeves, R., Bruster, S., & Richards, N. (2003). Properties of the Picker Patient Experience Questionnaire in a randomized controlled trial of long versus short form survey instruments. Journal of Public Health Medicine, 25, 197–201.CrossRefGoogle Scholar
  13. Jha, A. K., Orav, E. J., Zheng, J., & Epstein, A. M. (2008). Patients’ perception of hospital care in the United States. The New England Journal of Medicine, 359, 1921–1931.CrossRefGoogle Scholar
  14. Jolliffe, I. T. (2002). Principal component analysis. New York: Springer.Google Scholar
  15. Keller, A. C., Bergman, M. M., Heinzmann, C., Todorov, A., Weber, H., & Heberer, M. (2014). The relationship between hospital patients’ ratings of quality of care and communication. Internal Journal for Quality in Health Care, 26, 26–33.CrossRefGoogle Scholar
  16. Keller, S., O’Malley, A. J., Hays, R. D., Matthew, R. A., Zaslavsky, A. M., et al. (2005). Methods used to streamline the CAHPS Hospital Survey. Health Services Research, 40, 2057–2077.CrossRefGoogle Scholar
  17. Kleefstra, S. M., Kool, R. B., Veldkamp, C. M., Winters-van der Meer, A. C., Mens, M. A., et al. (2010). A core questionnaire for the assessment of patient satisfaction in academic hospitals in The Netherlands: Development and first results in a nationwide study. Quality and Safety in Health Care, 19, e24.Google Scholar
  18. Kong, G. L., Xu, D. L., Body, R., Yang, J. B., Mackway-Jones, K. R. H., & Carley, S. (2012). A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. European Journal of Operational Research, 219, 564–573.CrossRefGoogle Scholar
  19. Kong, G. L., Xu, D.-L., Liu, X., & Yang, J.-B. (2009). Applying a belief rule-base inference methodology to a guideline-based clinical decision support system. Expert Systems, 26, 391–408.CrossRefGoogle Scholar
  20. Kong, G. L., Xu, D.-L., Yang, J.-B., & Ma, X. M. (2015). Combined medical quality assessment using the evidential reasoning approach. Expert Systems with Applications, 42, 5522–5530.CrossRefGoogle Scholar
  21. Lyratzopoulos, G., Elliott, M. N., Barbiere, J. M., Staetsky, L., Paddison, C. A., et al. (2011). How can health care organizations be reliably compared? Lessons from a national survey of patient experience. Medical Care, 49, 724–733.CrossRefGoogle Scholar
  22. Morgan, R. (2017). An investigation of constraints upon fisheries diversification using the analytic hierarchy process (AHP). Marine Policy, 86, 24–30.CrossRefGoogle Scholar
  23. Norman, G. R., & Streiner, D. L. (1998). Biostatistics: The bare essentials. Hamilton: C. Decker Inc.Google Scholar
  24. Panagiotis, M., Kostas, K., & Ioannis, M. (2016). Factors affecting primary health care centers’ economic and production efficiency. Annals of Operations Research, 247, 807–822.CrossRefGoogle Scholar
  25. Park, Y. S., Egilmez, G., & Kucukvar, M. (2015). A novel life cycle-based principal component analysis framework for eco-efficiency analysis: Case of the United States manufacturing and transportation nexus. Journal of Cleaner Production, 92, 327–342.CrossRefGoogle Scholar
  26. Prior, D. (2006). Efficiency and total quality management in health care organizations: A dynamic frontier approach. Annals of Operations Research, 145, 281–299.CrossRefGoogle Scholar
  27. Purcărea, V. L., Gheorghe, I. R., & Petrescu, C. M. (2013). The assessment of perceived service quality of public health care services in Romania using the SERVQUAL scale. Procedia Economics and Finance, 6, 573–585.CrossRefGoogle Scholar
  28. Rodriguez, H., von Glahn, T., Elliott, M., Rogers, W., & Safran, D. (2009). The effect of performance-based financial incentives on improving patient care experiences: A statewide evaluation. Journal of General Internal Medicine, 24, 1281–1288.CrossRefGoogle Scholar
  29. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.Google Scholar
  30. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1, 83–98.CrossRefGoogle Scholar
  31. Vuković, M., Gvozdenović, B. S., Gajić, T., Stamatović Gajić, B., Jakovljević, M., & McCormick, B. P. (2012). Validation of a patient satisfaction questionnaire in primary health care. Public Health, 126, 710–718.CrossRefGoogle Scholar
  32. Wang, Y. M., Yang, J. B., & Xu, D. L. (2006). Environmental impact assessment using the evidential reasoning approach. European Journal of Operational Research, 174, 1885–1913.CrossRefGoogle Scholar
  33. Wong, E. L., Leung, M. C., Cheung, A. W., Yam, C. H., Yeoh, E. K., & Griffiths, S. (2011). A population-based survey using PPE-15: Relationship of care aspects to patient satisfaction in Hong Kong. International Journal for Quality in Health Care, 23, 390–396.CrossRefGoogle Scholar
  34. Xu, D.-L. (2012). An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis. Annals of Operations Research, 195, 163–187.CrossRefGoogle Scholar
  35. Xu, D. L., McCarthy, G., & Yang, J. B. (2006). Intelligent decision system and its application in business innovation self assessment. Decision Support Systems, 42, 664–673.CrossRefGoogle Scholar
  36. Yang, J. B. (2001). Rule and utility based evidential reasoning approach for multiple attribute decision analysis under uncertainty. European Journal of Operational Research, 131, 31–61.CrossRefGoogle Scholar
  37. Yang, J. B., & Singh, M. G. (1994). An evidential reasoning approach for multiple-attribute decision making with uncertainty. IEEE Transactions on Systems, Man and Cybernetics, 24, 1–18.CrossRefGoogle Scholar
  38. Yang, J. B., & Xu, D. L. (2002). On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 32, 289–304.CrossRefGoogle Scholar

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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