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Unveiling Hidden Patterns in Flexible Medical Treatment Processes – A Process Mining Case Study

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Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support (ICDSST 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 313))

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Abstract

In hospital environments, treatment processes, resp. clinical pathways, are adopted based on the health state of a patient. Modeling of pathways is time consuming and due to the involvement of many participants, the introduction of clinical pathways is cost-intensive. Process mining offers a possibility for automatic or semi-automatic creation of clinical pathways based on the event log data recorded during the process execution in hospital information systems. However, state-of-the-art algorithms struggle to discover meaningful end-to-end patterns from highly flexible clinical log data. This challenge can be addressed by Local Process Models. They allow pathways to be modeled partially, thus enabling the detection of major process steps. In our case study, we apply this recently proposed method on a real world clinical dataset and discuss results and challenges.

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Acknowledgment

The authors kindly thank Niek Tax, Ph.D. candidate at TU Eindhoven, the author of the LPM methodology, for his valuable help and advice on installing and running the novel LPM mining ProM nightly build plug-in, which was crucial for conducting the experimental part of this paper.

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Authors and Affiliations

  1. Berlin School of Economics and Law, 10315, Berlin, Germany

    Kathrin Kirchner

  2. Technische Universiteit Eindhoven, 5600, Eindhoven, The Netherlands

    Petar Marković

Authors
  1. Kathrin Kirchner
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  2. Petar Marković
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Corresponding author

Correspondence to Kathrin Kirchner .

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Editors and Affiliations

  1. SimTech Simulation Technology, Graz, Austria

    Fatima Dargam

  2. Eastern Macedonia and Thrace Institute of Technology, Kavala, Greece

    Pavlos Delias

  3. University of Namur, Namur, Belgium

    Isabelle Linden

  4. ULB, Brussels, Belgium

    Bertrand Mareschal

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Kirchner, K., Marković, P. (2018). Unveiling Hidden Patterns in Flexible Medical Treatment Processes – A Process Mining Case Study. In: Dargam, F., Delias, P., Linden, I., Mareschal, B. (eds) Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support. ICDSST 2018. Lecture Notes in Business Information Processing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-90315-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-90315-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90314-9

  • Online ISBN: 978-3-319-90315-6

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