Quality Assessment of the Oncology Health Service in a Public Hospital

  • Monica Palma
  • Veronica Distefano
  • Alessandra Spennato


Quality assessment is a crucial issue in the strategic management of the public health sector. The objective of this study is to investigate the patients’ perception of the health system quality and explore the relationships between doctors and long-term cancer patients. The data under study have been collected during a survey conducted with long-term cancer patients who follow an oncological therapy in a Public Hospital. In the study, exploratory factorial analysis is developed and two structural equation models are proposed. The first model describes the service quality as perceived by the patients, which is influenced by four important factors, namely tangible aspects, reliability, empathy (doctor–patient human relations) and hospital organization. The second model describes the relationship between doctors and long-term cancer patients, which is influenced by three factors, that is reliability, empathy and hospital organization. The discussion highlights the contribution that the results of the study may make to the investigation of the possible strategies for improving health care service quality.


Patients’ customer satisfaction Health system quality Exploratory factorial analysis Structural equation models 



The authors are grateful to the Editor and the reviewers, whose comments contribute to improve the paper. The authors thank Prof. A. Calogiuri for her useful contribution in reviewing the English usage.


  1. Anderson, L. A., & Dedrick, R. F. (1990). Development of the trust in physicians scale: A measure to assess interpersonal trust in patient-physician relationships. Psychological Reports, 67(3f), 1091–1100.Google Scholar
  2. Arbuckle, J. L. (1997). Amos users’guide version 3.6. Chicago: Small Waters Corporation.Google Scholar
  3. Babakus, E., & Mangold, W. G. (1992). Adapting the SERVQUAL scale to hospital services: An empirical investigation. Health Services Research, 26(6), 767–86.Google Scholar
  4. Barnett, V. (1991). Sample survey principles and methods. London: Edward Arnold.Google Scholar
  5. Bentler, P. M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin, 10(2), 238–246.CrossRefGoogle Scholar
  6. Bentler, P. M., & Bonett, D. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606.CrossRefGoogle Scholar
  7. Bollen, K. (1989). Structural equations with latent variables. New York: Wiley.CrossRefGoogle Scholar
  8. Bouranta, N., Chitiris, L., & Paravantis, J. (2009). The relationship between internal and external service quality. International Journal of Contemporary Hospitality Management, 21(3), 275–293.CrossRefGoogle Scholar
  9. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Sage Focus Editions, 154, 136–136.Google Scholar
  10. Byrne, B. (2001). Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International Journal of Testing, 1(1), 55–86.CrossRefGoogle Scholar
  11. Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276.CrossRefGoogle Scholar
  12. Cerny, C. A., & Kaiser, H. F. (1977). A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behavioral Research, 12(1), 43–47.CrossRefGoogle Scholar
  13. Child, D. (1990). The essentials of factor analysis (II ed.). New York: Cassell Educational.Google Scholar
  14. Cochran, W. G. (2007). Sampling techniques. New York: Wiley.Google Scholar
  15. Coenders, G., Batista-Foguet, J. M., & Saris, W. E. (2008). Simple, efficient and distribution-free approach to interaction effects in complex structural equation models. Quality & Quantity, 42(3), 369–396.CrossRefGoogle Scholar
  16. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.CrossRefGoogle Scholar
  17. Devlin, S. J., Dong, H. K., & Brown, M. (1993). Selecting a scale for measuring quality. Marketing Research, 5, 12–17.Google Scholar
  18. Dinc, L., Korkmaz, F., & Karabulut, E. (2013). A validity and reliability study of the Multidimensional Trust in Health-Care Systems Scale in a Turkish patient population. Social Indicators Research, 113(1), 107–120.CrossRefGoogle Scholar
  19. Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2(2), 119–143.CrossRefGoogle Scholar
  20. Donabedian, A. (1980). The definition of quality and approaches to its assessment. Health Services Research, 16(2), 236.Google Scholar
  21. Dzubian, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis. Psychological Bulletin, 81(6), 358–361.CrossRefGoogle Scholar
  22. Everitt, B., & Hothorn, T. (2011). An introduction to applied multivariate analysis with R. Berlin: Springer.CrossRefGoogle Scholar
  23. Finney, S. J., & DiStefano, C. (2008). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. D. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 269-314). Information Age Publishing.Google Scholar
  24. Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.Google Scholar
  25. Goldberger, A. S. (1972). Structural equation methods in the social sciences. Econometrica, 40(6), 979–1001.CrossRefGoogle Scholar
  26. Goldberger, A. S. (2008). Selection bias in evaluating treatment effects: Some formal illustrations. Advances in Econometrics, 21(1), 31.Google Scholar
  27. Guttman, L. (1954). A new approach to factor analysis: the Radex. In Paul F. Lazarsfeld (Ed.), Mathematical Thinking in the Social Sciences (pp. 258–348). New York: Free Press.Google Scholar
  28. Hall, M. A., Camacho, F., Dugan, E., & Balkrishnan, R. (2002). Trust in the medical profession: Conceptual and measurement issues. Health Service Research, 37(5), 1419–1439.CrossRefGoogle Scholar
  29. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
  30. Hatcher, L. (2005). A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary: SAS Institute.Google Scholar
  31. Hayes, B. E. (1992). Measurement Customer Satisfaction: Development and Use of Questionnaire. Milwaukee, WI: ASQC Quality Press.Google Scholar
  32. Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In R. H. Hoyle (Ed.), Structural equation modelling: Concepts, issues, and applications (pp. 1–15). Thousand Oaks, CA: Sage.Google Scholar
  33. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.CrossRefGoogle Scholar
  34. Hutcheson, G. D., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. London: Sage.CrossRefGoogle Scholar
  35. Intelligence, D. F. (2010). Intelligent Board 2010-patient experience. London: Dr Foster Intelligence.Google Scholar
  36. Joliffe, I. (2002). Principal component analysis. New York: Wiley.Google Scholar
  37. Jöreskog, K. G. (1973). A general method for estimating a linear structural equation system. In A. S. Goldberger & O. D. Duncan (Eds.), Structural equation models in the social sciences (pp. 85–112). New York, NY: Academic Press.Google Scholar
  38. Jöreskog, K. G. (1994) Structural equation modeling with ordinal variables. In Multivariate analysis and its applications (pp. 297–310). Hayward, CA: Institute of Mathematical Statistics.Google Scholar
  39. Joreskog, K. G. (1996). Applied factor analysis in the natural sciences. Cambridge: Cambridge University Press.Google Scholar
  40. Joreskog, K. G., Sorbom, D., Du Toit, S. H. C., Du Toit, M., & Stam, L. (2003). LISREL 8: New statistical features. Lincolnwood (III): Scientific Software International.Google Scholar
  41. Kaiser, H. F. (1970). A second generation little jiffy. Psychometrika, 35(4), 401–415.CrossRefGoogle Scholar
  42. Kao, A. C., Green, D. C., Davis, N. A., Koplan, J. P., & Cleary, P. D. (1998). Patients trust in their physicians. Journal of General Internal Medicine, 13(10), 681–686.CrossRefGoogle Scholar
  43. Kaplan, D. (2008). Structural equation modeling: Foundations and extensions. Thousand Oaks: Sage Publications.Google Scholar
  44. Kotler, P. R. (1997). Marketing management: Analysis, planning, implementation, and control (IX ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
  45. Linder-Pelz, S. (1982). Toward a theory of patient satisfaction. Social Science & Medicine, 16(5), 577–582.CrossRefGoogle Scholar
  46. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1(2), 130–149.CrossRefGoogle Scholar
  47. Marcoulides, G. A. (1998). Modern methods for business research. Hove: Psychology Press.Google Scholar
  48. Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11(3), 320–341.CrossRefGoogle Scholar
  49. Nitecki, D. A., & Hernon, P. (2007). Measuring service quality at yale university’s libraries. The Journal of Academic Librarianship, 24(4), 259–273.Google Scholar
  50. Nunnally, J. C. (1978). Psychometric theory (II ed.). New York: McGraw-Hill.Google Scholar
  51. Olckers, C., & van Zyl, L. (2016). The relationship between employment equity perceptions and psychological ownership in a south african mining house: The role of ethnicity. Social Indicators Research, 127(2), 887–901.CrossRefGoogle Scholar
  52. Ovretveit, J. (1992). Health Service Quality: An introduction to quality methods for health services. Oxford: Blackwell.Google Scholar
  53. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. The Journal of Marketing, 49, 41–50.CrossRefGoogle Scholar
  54. Pascoe, G. C. (1983). Patient satisfaction in primary health care: A literature review and analysis. Evaluation Program Planning, 6(3), 185–210.CrossRefGoogle Scholar
  55. Radwin, L. E., & Cabral, H. J. (2010). Trust in nurses scale: Construct validity and internal reliability evaluation. Journal of Advanced Nursing, 66(3), 683–689.CrossRefGoogle Scholar
  56. Roshnee Ramsaran-Fowdar, R. (2008). The relative importance of service dimensions in a health care setting. International Journal of Health Care Quality Assurance, 21(1), 104–124.CrossRefGoogle Scholar
  57. Saris, W. (1982). Different questions, different variables (pp. 78–95). New York: Preager.Google Scholar
  58. Schuster, M. A., McGlynn, E. A., & Brook, R. H. (1998). How good is the quality of health care in the United States? Milbank Quarterly, 76(4), 517–563.CrossRefGoogle Scholar
  59. Shevlin, M., & Miles, J. N. (1998). Effects of sample size, model specification and factor loadings on the GFI in confirmatory factor analysis. Personality and Individual Differences, 25(1), 85–90.CrossRefGoogle Scholar
  60. Steiger, J. H., & Lind, J. C. (1980). Statistically-based tests for the number of common factors. In Paper presented at the annual Spring Meeting of the Psychometric Society in Iowa City, May 30, 1980.Google Scholar
  61. Tinsley, H. E., & Brown, S. D. (2000). Handbook of applied multivariate statistics and mathematical modeling. London: Academic Press.Google Scholar
  62. Ullman, J. B., & Bentler, P. M. (2003). Structural equation modeling. New York: Wiley.CrossRefGoogle Scholar
  63. Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. New York: Wiley.CrossRefGoogle Scholar
  64. Werts, C. E., Linn, R. L., & Joreskog, K. G. (1974). Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological measurement, 34(1), 25–33.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Monica Palma
    • 1
  • Veronica Distefano
    • 1
  • Alessandra Spennato
    • 2
  1. 1.Dip. Scienze dell’EconomiaUniversità del SalentoLecceItaly
  2. 2.Core LabUniversità del SalentoLecceItaly

Personalised recommendations