Advertisement

Mining and Retrieving Medical Processes to Assess the Quality of Care

  • Stefania Montani
  • Giorgio Leonardi
  • Silvana Quaglini
  • Anna Cavallini
  • Giuseppe Micieli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7969)

Abstract

In a competitive healthcare market, hospitals have to focus on ways to deliver high quality care while at the same time reducing costs. To accomplish this goal, hospital managers need a thorough understanding of the actual processes. Process mining can be used to extract process related information (e.g., process models) from data. This process information can be exploited to understand and redesign processes to become efficient high quality processes. Process analysis and redesign can take advantage of Case Based Reasoning techniques.

In this paper, we present a framework that applies process mining and case retrieval techniques, relying on a novel distance measure, to stroke management processes. Specifically, the goal of the framework is the one of analyzing the quality of stroke management processes, in order to verify: (i) whether different patient categories are differently treated (as expected), and (ii) whether hospitals of different levels (defined by the absence/presence of specific resources) actually implement different processes (as they auto-declare). Some first experimental results are presented and discussed.

Keywords

Business Process Process Mining Lombardia Region Taxonomic Distance Graph Edit Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and systems approaches. AI Communications 7, 39–59 (1994)Google Scholar
  2. 2.
    Bergmann, R., Gil, Y.: Retrieval of semantic workflows with knowledge intensive similarity measures. In: Ram, A., Wiratunga, N. (eds.) ICCBR 2011. LNCS, vol. 6880, pp. 17–31. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recognition Letters 18(8), 689–694 (1997)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Van der Aalst, W., van Dongen, B., Herbst, J., Maruster, L., Schimm, G., Weijters, A.: Workflow mining: a survey of issues and approaches. Data and Knowledge Engineering 47, 237–267 (2003)CrossRefGoogle Scholar
  5. 5.
    Dijkman, R., Dumas, M., Garca-Banuelos, R.: Graph matching algorithms for business process model similarity search. In: Proc. International Conference on Business Process Management, pp. 48–63 (2009)Google Scholar
  6. 6.
    IEEE Taskforce on Process Mining: Process Mining Manifesto, http://www.win.tue.nl/ieeetfpm
  7. 7.
    Kapetanakis, S., Petridis, M., Knight, B., Ma, J., Bacon, L.: A case based reasoning approach for the monitoring of business workflows. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 390–405. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Kendall-Morwick, J., Leake, D.: On tuning two-phase retrieval for structured cases. In: Lamontagne, L., Recio-García, J.A. (eds.) Proc. ICCBR 2012 Workshops, pp. 25–334 (2012)Google Scholar
  9. 9.
    Mans, R., Schonenberg, H., Leonardi, G., Panzarasa, S., Cavallini, A., Quaglini, S., Van der Aalst, W.: Aprocess mining techniques: an application to stroke care. In: Proc. Medical Informatics Europe (MIE), pp. 573–578 (2008)Google Scholar
  10. 10.
    Minor, M., Tartakovski, A., Schmalen, D., Bergmann, R.: Agile workflow technology and case-based change reuse for long-term processes. International Journal of Intelligent Information Technologies 4(1), 80–98 (2008)CrossRefGoogle Scholar
  11. 11.
    Montani, S.: Prototype-based management of business process exception cases. Applied Intelligence 33, 278–290 (2010)CrossRefGoogle Scholar
  12. 12.
    Montani, S., Leonardi, G.: Retrieval and clustering for supporting business process adjustment and analysis. Information Systems, doi: http://dx.doi.org/10.1016/j.is.2012.11.006
  13. 13.
    Montani, S., Leonardi, G.: Retrieval and clustering for business process monitoring: results and improvements. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS, vol. 7466, pp. 269–283. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W(E.), Weijters, A.J.M.M.T., van der Aalst, W.M.P.: The proM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Weber, B., Wild, W.: Towards the agile management of business processes. In: Althoff, K.-D., Dengel, A.R., Bergmann, R., Nick, M., Roth-Berghofer, T.R. (eds.) WM 2005. LNCS (LNAI), vol. 3782, pp. 409–419. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Weijters, A., Van der Aalst, W., Alves de Medeiros, A.: Process Mining with the Heuristic Miner Algorithm, BETA Working Paper Series, WP 166. Eindhoven University of Technology, Eindhoven (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefania Montani
    • 1
  • Giorgio Leonardi
    • 1
    • 2
  • Silvana Quaglini
    • 2
  • Anna Cavallini
    • 3
  • Giuseppe Micieli
    • 3
  1. 1.DISIT, Computer Science InstituteUniversità del Piemonte OrientaleAlessandriaItaly
  2. 2.Dipartimento di Informatica e SistemisticaUniversità di PaviaItaly
  3. 3.IRCCS Fondazione “C. Mondino”, (Stroke Unit Network (SUN) collaborating centers)PaviaItaly

Personalised recommendations