Process Mining in Healthcare

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https://hdl.handle.net/10125/102540

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    Digital Health Data Imperfection Patterns and Their Manifestations in an Australian Digital Hospital
    ( 2023-01-03) Goel, Kanika ; Sadeghianasl, Sareh ; Andrews, Robert ; Ter Hofstede, Arthur ; Wynn, Moe ; Kapugama Geeganage, Dakshi ; Leemans, Sander ; Mcgree, James ; Eden, Rebekah ; Staib, Andrew ; Eley, Rob ; Donovan, Raelene
    Whilst digital health data provides great benefits for improved and effective patient care and organisational outcomes, the quality of digital health data can sometimes be a significant issue. Healthcare providers are known to spend a significant amount of time on assessing and cleaning data. To address this situation, this paper presents six Digital Health Data Imperfection Patterns that provide insight into data quality issues of digital health data, their root causes, their impact, and how these can be detected. Using the CRISP-DM methodology, we demonstrate the utility and pervasiveness of the patterns at the emergency department of Australia's major tertiary digital hospital. The pattern collection can be used by health providers to identify and prevent key digital health data quality issues contributing to reliable insights for clinical decision making and patient care delivery. The patterns also provide a solid foundation for future research in digital health through its identification of key data quality issues, root causes, detection techniques, and terminology.
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    Data-Based Process Variant Analysis
    ( 2023-01-03) Cremerius, Jonas ; Patzlaff, Hendrik ; Rahn, Vincent ; Leopold, Henrik
    Processes in healthcare are complex and data-intensive. Process mining uses data recorded during process execution to obtain an understanding of the actual execution of a process. Due to the complexity of healthcare processes, it is useful to consider and analyse the process execution of certain cohorts, such as old and young patients, separately. While such analysis is facilitated by process variant analysis techniques, existing approaches for process variant analysis only consider a comparison based on the control flow and performance perspectives. Given the large amount of event data attributes available in healthcare settings, we propose the first data-based process variant analysis approach. Our approach allows comparing process variants based on differences in event data attributes by building on statistical tests. We applied our approach on the MIMIC-IV real-world data set on hospitalizations in the US, where we demonstrate that the approach is feasible and can actually provide relevant medical insights.
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    Introduction to the Minitrack on Process Mining in Healthcare
    ( 2023-01-03) Weske, Mathias ; Pufahl, Luise ; Munoz-Gama, Jorge
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    From Insights to INTEL: Evaluating Process Mining Insights with Healthcare Professionals
    ( 2023-01-03) Beerepoot, Iris ; Martin, Niels ; Koorn, Jelmer Jan
    As healthcare organisations are looking for ways to improve their processes, process mining techniques are increasingly being used. Current process mining methods do not offer support for translating process mining insights into actionable improvement ideas. By performing action research at two healthcare organisations, we introduce and illustrate the INTEL funnel, a novel three-staged method consisting of process familiarisation, domain explanation and improvement ideation. Our method complements existing process mining methods and constitutes the first attempt to open the black box regarding the path from process mining insights to actionable process improvement ideas. In this way, it can contribute to a more systematic uptake of process mining in healthcare practice.