Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare†
1
Research Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria
2
Institute for Applied Knowledge Processing, Johannes Kepler University, 4040 Linz, Austria
3
Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
*
Authors to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in International Workshop on Process-Oriented Data Science for Healthcare 2019 (PODS4H19), Vienna, Austria. September 2, 2019 called “Adopting Standard Clinical Descriptors for Process Mining Case Studies in Healthcare”.
Int. J. Environ. Res. Public Health 2020, 17(4), 1348; https://doi.org/10.3390/ijerph17041348
Received: 5 January 2020 / Revised: 9 February 2020 / Accepted: 14 February 2020 / Published: 19 February 2020
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))
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.
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Keywords:
process mining; healthcare; terminology; ICD; SNOMED
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MDPI and ACS Style
Helm, E.; Lin, A.M.; Baumgartner, D.; Lin, A.C.; Küng, J. Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare. Int. J. Environ. Res. Public Health 2020, 17, 1348.
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