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

Emmanuel Helm, Anna Maria Lin, David Baumgartner, Alvin Curtis Lin, Josef Küng

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article number1348
JournalInternational Journal of Environmental Research and Public Health
Volume17
Issue number4
DOIs
Publication statusPublished - 2 Feb 2020

Keywords

  • Healthcare
  • ICD
  • Process mining
  • SNOMED
  • Terminology
  • Medicine
  • Algorithms
  • Delivery of Health Care
  • Humans
  • Clinical Coding

Fingerprint

Dive into the research topics of 'Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare'. Together they form a unique fingerprint.

Cite this