Assessing the Efficiency of Health Care Providers: A SOM Perspective

  • Marina Resta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6731)


We explored the use of Self Organizing Map (SOM) to assess the problem of efficiency measurement in the case of health care providers. To do this, we used as input the data from the balance sheets of 300 health care providers, as resulting from the Italian Statistics Institute (ISTAT) database, and we examined their representation obtained both by running classical SOM algorithm, and by modifying it through the replacement of standard Euclidean distance with the generalized Minkowski metrics. Finally, we have shown how the results may be employed to perform graph mining on data. In this way, we were able to discover intrinsic relationships among health care providers that, in our opinion, can be of help to stakeholders to improve the quality of health care service. Our results seem to contribute to the existing literature in at least two ways: (a) using SOM to analyze data of health care providers is completely new; (b) SOM graph mining shows, in turn, elements of innovations for the way the adjacency matrix is formed, with the connections among SOM winner nodes used as starting point to the process.


SOM Network Representation Efficiency Health Care Providers 


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  1. 1.
    Aggarwal, C.C., Yu, P.S.: The IGrid Index: Reversing the Dimensionality Curse For Similarity Indexing in High Dimensional Space. In: Proc. of KDD, pp. 119–129 (2000)Google Scholar
  2. 2.
    Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the Surprising Behavior of Distance Metrics in High Dimensional Space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Bjorkgren, M., Hakkinen, U., Linna, M.: Measuring Efficiency of Long Term Care Units in Finland. Health Care Management Science 4(3), 193–201 (2001)CrossRefGoogle Scholar
  4. 4.
    Braithwaite, J., Westbrook, M., Hindle, D., Ledema, R., Black, D.: Does Restructuring Hospitals Result in Greater Efficiency?-an Empirical Test Using Diachronic Data. Health Services Management Research 19(1), 1–13 (2006)CrossRefGoogle Scholar
  5. 5.
    Banker, R.: Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation. Management Science 39(10), 1265–1273 (1993)CrossRefzbMATHGoogle Scholar
  6. 6.
    Boulet, R., Jouve, B., Rossi, F., Villa, N.: Batch kernel SOM and related Laplacian methods for social network analysis. Neurocomputing 71(7-9), 1257–1273 (2008)CrossRefGoogle Scholar
  7. 7.
    Demartines, P.: Analyse de Données par Réseaux de Neurones Auto-Organisés. PhD dissertation, Institut Nat’l Polytechnique de Grenoble, Grenoble, France (1994)Google Scholar
  8. 8.
    Francois, D., Wertz, V., Verleysen, M.: Non-euclidean metrics for similarity search in noisy datasets. In: Proc. of ESANN 2005, European Symposium on Artificial Neural Networks (2005)Google Scholar
  9. 9.
    Hollingsworth, B.: Non-Parametric and Parametric Applications Measuring Efficiency in Health Care. Health Care Management Science 6(4), 203–218 (2003)CrossRefGoogle Scholar
  10. 10.
    Hurley, E., McRae, I., Bigg, I., Stackhouse, L., Boxall, A.M., Broadhead, P.: The Australian health care system: the potential for efficiency gains. In: Working paper, Australian Government National Health and Hospitals Reform Commission (2009)Google Scholar
  11. 11.
    Key, B., Reed, R., Sclar, D.: First-order Economizing: Organizational Adaptation and the Elimination of Waste in the U.S. Pharmaceutical Industry. Journal of Managerial Issues 17(4), 511–528 (2005)Google Scholar
  12. 12.
    Kohonen, T.: Self-Organizing Maps. Springer, Berlin (2002)zbMATHGoogle Scholar
  13. 13.
    Murillo Zamorano, L.: Economic Efficiency and Frontier Techniques. Journal of Economic Surveys 18(1), 33–77 (2004)CrossRefGoogle Scholar
  14. 14.
    Resta, M.: Seize the (intra)day: Features selection and rules extraction for tradings on high-frequency data. Neurocomputing 72(16-18), 3413–3427 (2009)CrossRefGoogle Scholar
  15. 15.
    Resta, M.: On the Impact of the Metrics Choice in SOM Learning: Some Empirical Results from Financial Data. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010. LNCS, vol. 6278, pp. 583–591. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Tumminello, M., Aste, T., Di Matteo, T., Mantegna, R.N.: A tool for filtering information in complex systems. PNAS 102(30), 10421–10426 (2005)CrossRefGoogle Scholar
  17. 17.
    Verleysen, M., Francois, D.: The Concentration of Fractional Distances. IEEE Trans. on Knowledge and Data Engineering 19(7), 873–886 (2007)CrossRefGoogle Scholar
  18. 18.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab 5. Helsinki University of Technology Technical Report (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marina Resta
    • 1
  1. 1.DIEM sezione Matematica FinanziariaUniversity of GenovaGenovaItaly

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