scholarly journals An Expert Lens on Data Quality in Process Mining

2020 ◽  
Author(s):  
Robert Andrews ◽  
Fahame Emamjome ◽  
Arthur H.M. ter Hofstede ◽  
Hajo A. Reijers
Keyword(s):  
Data Quality ◽  
Process Mining

Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance

2016 ◽  
Vol 22 (4) ◽  
pp. 1017-1029 ◽  
Author(s):  
Lua Perimal-Lewis ◽  
David Teubner ◽  
Paul Hakendorf ◽  
Chris Horwood
Keyword(s):  
Electronic Health Records ◽  
Data Quality ◽  
Performance Metrics ◽  
Process Mining ◽  
Hospital Performance ◽  
Process Models ◽  
Patient Journey ◽  
Health Records ◽  
Electronic Health

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

scholarly journals Data Quality and Completeness in a Web Stroke Registry as the Basis for Data and Process Mining

2014 ◽  
Vol 5 (2) ◽  
pp. 163-184 ◽  
Author(s):  
Giordano Lanzola ◽  
Enea Parimbelli ◽  
Giuseppe Micieli ◽  
Anna Cavallini ◽  
Silvana Quaglini
Keyword(s):  
Data Quality ◽  
Process Mining ◽  
Stroke Registry

Data Quality Assessment to Apply Process Mining in Production Processes

2021 ◽  
pp. 515-524
Author(s):  
Henning Neumann ◽  
Moritz Müller-Roden ◽  
Seth Schmitz ◽  
Andreas Gützlaff ◽  
Günther Schuh
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Process Mining ◽  
Production Processes ◽  
Data Quality Assessment

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

2019 ◽  
Vol 16 (7) ◽  
pp. 1138 ◽  
Author(s):  
Robert Andrews ◽  
Moe Wynn ◽  
Kirsten Vallmuur ◽  
Arthur ter Hofstede ◽  
Emma Bosley ◽  
...  
Keyword(s):  
Data Quality ◽  
Process Mining ◽  
Data Extraction ◽  
Case Study Research ◽  
Poor Quality ◽  
Transport Processes ◽  
Quality Data ◽  
Trauma Patients ◽  
Quality Issues

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.

scholarly journals Getting There: Evidence-Based Decision-Making in Road Trauma Prehospital Transport and Care in Queensland

2019 ◽  
Vol 34 (s1) ◽  
pp. s64-s65
Author(s):  
Robert Andrews ◽  
Moe Wynn ◽  
Arthur ter Hofstede ◽  
Kirsten Vallmuur ◽  
Emma Bosley ◽  
...  
Keyword(s):  
Emergency Department ◽  
Data Quality ◽  
Data Science ◽  
Hospital Admissions ◽  
Process Mining ◽  
Transport Processes ◽  
Process Models ◽  
Actual Behavior ◽  
Patient Pathways ◽  
Quality Issues

Introduction:Process mining, a branch of data science, aims at deriving an understanding of process behaviors from data collected during executions of the process. In this study, we apply process mining techniques to examine retrieval and transport of road trauma patients in Queensland. Specifically, we use multiple datasets collected from ground and air ambulance, emergency department, and hospital admissions to investigate the various patient pathways and transport modalities from accident to definitive care.Aim:The project aims to answer the question, “Are we providing the right level of care to patients?” We focus on (i) automatically discovering, from historical records, the different care and transport processes, and (ii) identifying and quantifying factors influencing deviance from standard processes, e.g. mechanisms of injury and geospatial (crash and trauma facility) considerations.Methods:We adapted the Cross-Industry Standard Process for Data Mining methodology to Queensland Ambulance Service, Retrieval Services Queensland (aero-medical), and Queensland Health (emergency department and hospital admissions) data. Data linkage and “case” definition emerged as particular challenges. We developed detailed data models, conduct a data quality assessment, and preliminary process mining analyses.Results:Preliminary results only with full results are presented at the conference. A collection of process models, which revealed multiple transport pathways, were automatically discovered from pilot data. Conformance checking showed some variations from expected processing. Systematic analysis of data quality allowed us to distinguish between systemic and occasional quality issues, and anticipate and explain certain observable features in process mining analyses. Results will be validated with domain experts to ensure insights are accurate and actionable.Discussion:Preliminary analysis unearthed challenging data quality issues that impact the use of historical retrieval data for secondary analysis. The automatically discovered process models will facilitate comparison of actual behavior with existing guidelines.

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