Embedding Conformance Checking in a Process Intelligence System in Hospital Environments

  • Kathrin Kirchner
  • Nico Herzberg
  • Andreas Rogge-Solti
  • Mathias Weske
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7738)

Abstract

Process intelligence is an effective means to analyze and improve business processes in companies with high degree of automation. Hospitals are also facing high pressure to be profitable with ever decreasing available funds in a stressed healthcare sector, which calls for methods to enable process management and intelligent methods in their daily work. However, traditional process intelligence systems work with logs of execution data that is generated by workflow engines controlling the execution of a process. But the nature of the treatment processes requires the doctors to work with a high freedom of action, rendering workflow engines unusable in this context.

In this paper, we introduce a novel method to conformance checking that computes fitness of individual activities in the setting of sparse process execution information, i.e., not all activities of a patient’s treatment are logged. We embed this method into a process intelligence approach for hospitals without workflow engines, enabling process monitoring and analysis.

Keywords

process modeling in healthcare visualization and monitoring healthcare processes conformance checking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Conformance checking using cost-based fitness analysis. In: EDOC 2011, pp. 55–64. IEEE (2011)Google Scholar
  2. 2.
    Azvine, B., Cui, Z., Nauck, D.D., Majeed, B.: Real Time Business Intelligence for the Adaptive Enterprise. In: CEC/EEE 2006, p. 29 (2006)Google Scholar
  3. 3.
    Bobrik, R.: Konfigurierbare Visualisierung komplexer Prozessmodelle. PhD thesis, University of Ulm (2008)Google Scholar
  4. 4.
    Dahanayake, A., Welke, R.J., Cavalheiro, G.: Improving the understanding of BAM technology for real-time decision support. Int. J. Bus. Inf. Syst. 7, 1–26 (2011)Google Scholar
  5. 5.
    Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.-C.: Business process intelligence. Computers in Industry 53(3), 321–343 (2004)CrossRefGoogle Scholar
  6. 6.
    Grimshaw, J.M., Russell, I.T.: Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. The Lancet 342(8883), 1317–1322 (1993)CrossRefGoogle Scholar
  7. 7.
    Herzberg, N., Kunze, M., Rogge-Solti, A.: Towards process evaluation in non-automated process execution environments. In: Proceedings of the 4th Central-European Workshop on Services and their Composition, ZEUS 2012, pp. 96–102. CEUR-WS.org (2012)Google Scholar
  8. 8.
    Köth, H., Miller, K., Lein, M., et al.: Entwicklung und Effekte eines standortübergreifenden klinischen Behandlungspfades am Beispiel: ”Laparoskopische Prostatektomie”. Perioperative Medizin 1(3), 173–180 (2009)CrossRefGoogle Scholar
  9. 9.
    Lohmann, N., Verbeek, E., Dijkman, R.: Petri Net Transformations for Business Processes – A Survey. In: Jensen, K., van der Aalst, W.M.P. (eds.) ToPNoC II. LNCS, vol. 5460, pp. 46–63. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Luebbe, A., Weske, M.: Tangible Media in Process Modeling – A Controlled Experiment. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 283–298. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Macario, A., Vitez, T.S., Dunn, B., McDonald, T.: Where are the costs in perioperative care?: Analysis of hospital costs and charges for inpatient surgical care. Anesthesiology 83(6), 1138 (1995)CrossRefGoogle Scholar
  12. 12.
    Mans, R., Reijers, H., van Genuchten, M., Wismeijer, D.: Mining processes in dentistry. In: Proceedings of the 2nd ACM SIGHIT Symposium on International Health Informatics, pp. 379–388. ACM (2012)Google Scholar
  13. 13.
    Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of Process Mining in Healthcare – A Case Study in a Dutch Hospital. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2008. CCIS, vol. 25, pp. 425–438. Springer, Heidelberg (2008)Google Scholar
  14. 14.
    Melchert, F., Winter, R., Klesse, M., Romano Jr., N.C.: Aligning process automation and business intelligence to support corporate performance management. In: AMCIS, New York, pp. 4053–4063 (2004)Google Scholar
  15. 15.
    Montani, S., Leonardi, G.: A Case-Based Approach to Business Process Monitoring. In: Bramer, M. (ed.) IFIP AI 2010. IFIP AICT, vol. 331, pp. 101–110. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Mutschler, B., Reichert, M.: Aktuelles schlagwort: Business process intelligence. EMISA Forum 26(1), 27–31 (2006)Google Scholar
  17. 17.
    Object Management Group. Business Process Model and Notation (BPMN) Specification, Version 2.0 (2011)Google Scholar
  18. 18.
    Raetzell, M., Bauer, M.: Standard operating procedures und klinische behandlungspfade. In: OP-Management: Praktisch und Effizient, pp. 187–198. Springer (2006)Google Scholar
  19. 19.
    Rebuge, Á., Ferreira, D.R.: Business Process Analysis in Healthcare Environments: A Methodology based on Process Mining. Information Systems (2011)Google Scholar
  20. 20.
    Rogge-Solti, A., Weske, M.: Enabling Probabilistic Process Monitoring in Non-automated Environments. In: Bider, I., Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Wrycza, S. (eds.) BPMDS 2012 and EMMSAD 2012. LNBIP, vol. 113, pp. 226–240. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Ronellenfitsch, U., Rössner, E., Jakob, J., Post, S., Hohenberger, P., Schwarzbach, M.: Clinical pathways in surgery – should we introduce them into clinical routine? a review article. Langenbeck’s Archives of Surgery 393(4), 449–457 (2008)CrossRefGoogle Scholar
  22. 22.
    Ronellenfitsch, U., Schwarzbach, M.: Klinisches Prozessmanagement - Klinische Pfade in der Chirurgie: Evidenz und Potenzial. Zentralblatt Chirurgie 135(2), 99–101 (2010)CrossRefGoogle Scholar
  23. 23.
    Rotter, T., et al.: Clinical pathways: effects on professional practice, patient outcomes, length of stay and hospital costs. Cochrane Database Syst. Rev. (3) (2010)Google Scholar
  24. 24.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)CrossRefGoogle Scholar
  25. 25.
    Uerlich, M., Dahmen, A., Tuschy, S., Ronellenfitsch, U., Eveslage, K., Vargas Hein, O., Tuerk-Ihli, G., Schwarzbach, M.: Klinische Pfade - Terminologie und Entwicklungsstufen. Periop. Med. 1(3), 155–163 (2009)CrossRefGoogle Scholar
  26. 26.
    van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes, 1st edn. Springer (2011)Google Scholar
  27. 27.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., 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
  28. 28.
    Weske, M.: Business Process Management: Concepts, Languages, Architectures, 2nd edn. Springer (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kathrin Kirchner
    • 1
  • Nico Herzberg
    • 2
  • Andreas Rogge-Solti
    • 2
  • Mathias Weske
    • 2
  1. 1.University Hospital of JenaJenaGermany
  2. 2.Hasso Plattner InstituteUniversity of PotsdamPotsdamGermany

Personalised recommendations