Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland.

Robert Andrews,Moe Wynn,Mark Elcock,Emma Bosley,Stephen Rashford,Arthur Ter Hofstede,Kirsten Vallmuur

doi:10.3390/ijerph16071138

Abstract

While noting the importance of data quality, existing process mining methodologies (i) do not provide details on how to assess the quality of event data (ii) do not consider how the identification of data quality issues can be exploited in the planning, data extraction and log building phases of any process mining analysis, (iii) do not highlight potential impacts of poor quality data on different types of process analyses. As our key contribution, we develop a process-centric, data quality-driven approach to preparing for a process mining analysis which can be applied to any existing process mining methodology. Our approach, adapted from elements of the well known CRISP-DM data mining methodology, includes conceptual data modeling, quality assessment at both attribute and event level, and trial discovery and conformance to develop understanding of system processes and data properties to inform data extraction. We illustrate our approach in a case study involving the Queensland Ambulance Service (QAS) and Retrieval Services Queensland (RSQ). We describe the detailed preparation for a process mining analysis of retrieval and transport processes (ground and aero-medical) for road-trauma patients in Queensland. Sample datasets obtained from QAS and RSQ are utilised to show how quality metrics, data models and exploratory process mining analyses can be used to (i) identify data quality issues, (ii) anticipate and explain certain observable features in process mining analyses, (iii) distinguish between systemic and occasional quality issues, and (iv) reason about the mechanisms by which identified quality issues may have arisen in the event log. We contend that this knowledge can be used to guide the data extraction and pre-processing stages of a process mining case study to properly align the data with the case study research questions.

Highlights

Process mining is a maturing discipline and includes a set of tools and techniques to visualise process-related data
In this paper we present a process-centric, data quality-driven approach to preparing for a process mining analysis which can be applied to any existing process mining methodology
We utilise sample datasets from Queensland Ambulance Service (QAS) and Retrieval Services Queensland (RSQ) to show how data quality metrics, data models and exploratory process mining analyses can be used to (i) identify data quality issues, (ii) anticipate and explain certain observable features in process mining analyses, (iii) distinguish between systemic and occasional quality issues, and (iv) reason about the mechanisms by which identified quality issues may have arisen in the event log

Summary

Introduction

Process mining is a maturing discipline and includes a set of tools and techniques to visualise process-related data. Process mining is being increasingly applied to healthcare Report on 74 process mining papers/case studies in healthcare, Yang and Su’s [2] report on 37 studies from 2004–2013 that apply process mining to various aspects of clinical pathways). Some studies have shown that electronic medical records suffer data quality issues [3,4,5] which may impact on their usefulness in research. The delivery of appropriate and timely pre-hospital care and transport of seriously injured road trauma patients is critical to patient survival and outcomes. Pre-hospital care and transport can be supplied by ground-based services, by aero-medical services, or by a combination of these two services

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