Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare
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 sever...
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doaj-1f23aee6efdb46b3a8aa0e21c130894d2020-11-25T01:47:07ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012020-02-01174134810.3390/ijerph17041348ijerph17041348Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in HealthcareEmmanuel Helm0Anna M. Lin1David Baumgartner2Alvin C. Lin3Josef Küng4Research Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, AustriaResearch Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, AustriaResearch Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, AustriaFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, CanadaInstitute for Applied Knowledge Processing, Johannes Kepler University, 4040 Linz, AustriaProcess 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.https://www.mdpi.com/1660-4601/17/4/1348process mininghealthcareterminologyicdsnomed |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Emmanuel Helm Anna M. Lin David Baumgartner Alvin C. Lin Josef Küng |
spellingShingle |
Emmanuel Helm Anna M. Lin David Baumgartner Alvin C. Lin Josef Küng Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare International Journal of Environmental Research and Public Health process mining healthcare terminology icd snomed |
author_facet |
Emmanuel Helm Anna M. Lin David Baumgartner Alvin C. Lin Josef Küng |
author_sort |
Emmanuel Helm |
title |
Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare |
title_short |
Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare |
title_full |
Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare |
title_fullStr |
Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare |
title_full_unstemmed |
Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare |
title_sort |
towards the use of standardized terms in clinical case studies for process mining in healthcare |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1660-4601 |
publishDate |
2020-02-01 |
description |
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. |
topic |
process mining healthcare terminology icd snomed |
url |
https://www.mdpi.com/1660-4601/17/4/1348 |
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