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Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

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  • Emmanuel Helm

    (Research Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria
    Institute for Applied Knowledge Processing, Johannes Kepler University, 4040 Linz, Austria)

  • Anna M. Lin

    (Research Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria)

  • David Baumgartner

    (Research Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria)

  • Alvin C. Lin

    (Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada)

  • Josef Küng

    (Institute for Applied Knowledge Processing, Johannes Kepler University, 4040 Linz, Austria)

Abstract

Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article.

Suggested Citation

  • Emmanuel Helm & Anna M. Lin & David Baumgartner & Alvin C. Lin & Josef Küng, 2020. "Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare," IJERPH, MDPI, vol. 17(4), pages 1-12, February.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:4:p:1348-:d:322624
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    References listed on IDEAS

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    1. Hyunyoung Baek & Minsu Cho & Seok Kim & Hee Hwang & Minseok Song & Sooyoung Yoo, 2018. "Analysis of length of hospital stay using electronic health records: A statistical and data mining approach," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    2. Christoph Rinner & Emmanuel Helm & Reinhold Dunkl & Harald Kittler & Stefanie Rinderle-Ma, 2018. "Process Mining and Conformance Checking of Long Running Processes in the Context of Melanoma Surveillance," IJERPH, MDPI, vol. 15(12), pages 1-14, December.
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    Cited by:

    1. Dylan A. Mordaunt, 2020. "On Clinical Utility and Systematic Reporting in Case Studies of Healthcare Process Mining. Comment on: 10.3390/ijerph17041348 “Towards the Use of Standardised Terms in Clinical Case Studies for Proces," IJERPH, MDPI, vol. 17(22), pages 1-4, November.

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