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 T. Wynn, Kirsten Vallmuur, Arthur H. M. ter Hofstede, Emma Bosley, Mark Elcock and Stephen Rashford
Additional contact information
Robert Andrews: School of Information Systems, Queensland University of Technology (QUT), Brisbane 4000, Australia
Moe T. Wynn: School of Information Systems, Queensland University of Technology (QUT), Brisbane 4000, Australia
Kirsten Vallmuur: Institute of Health and Biomedical Innovation and School of Public Health and Social Work, Queensland University of Technology (QUT), Brisbane 4059, Australia
Arthur H. M. ter Hofstede: School of Information Systems, Queensland University of Technology (QUT), Brisbane 4000, Australia
Emma Bosley: Queensland Ambulance Service (QAS), Brisbane 4034, Australia
Mark Elcock: Retrieval Services Queensland (RSQ), Brisbane 4000, Australia
Stephen Rashford: Queensland Ambulance Service (QAS), Brisbane 4034, Australia

IJERPH, 2019, vol. 16, issue 7, 1-25

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.

Keywords: process mining in healthcare; methodologies and best practice for PODS4H; data quality; pre-hospital transport and care; GEMS; HEMS (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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