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.

Highlights

  • Process mining is a discipline that allows for greater understanding into real-life processes of recorded systems behaviour

  • Recent review papers have provided an overview of process mining across clinical case studies

  • Our paper focused on answering three questions: (1) Which clinically-relevant case studies of process mining in healthcare will be selected for this study? (2) What were the technical aspects identified? (3) How can we improve the clarity and comparability of the clinical terms and aspects described?

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Summary

Introduction

Process mining is a discipline that allows for greater understanding into real-life processes of recorded systems behaviour. Recent review papers have provided an overview of process mining across clinical case studies. Rojas et al in 2016 identified eleven common aspects across 74 clinical case studies [1]. These aspects include methodologies, techniques or algorithms, medical fields and healthcare specialty. In 2018, Erdogan and Tarhan conducted a systematic mapping of 172 case studies with mostly the same metrics and aspects [2]. These papers are very specific as to how these case studies were conducted, which enhances comparison between different process mining techniques in different settings. From a medical perspective, the terms and categories listed under medical fields

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