Process Mining-Driven Analysis of the COVID19 Impact on the Vaccinations of Victorian Patients
Adriano Augusto, Timothy Deitz, Noel Faux, Jo-Anne Manski-Nankervis,, Daniel Capurro

TL;DR
This study applies process mining to analyze healthcare process data from Victoria, Australia, revealing that vaccination rates did not decline during COVID-19, but instead surged, contrasting with other regions.
Contribution
The paper demonstrates the application of process mining techniques to large healthcare datasets to analyze COVID-19's impact on vaccination patterns, highlighting benefits and limitations.
Findings
Vaccination rates did not decline during COVID-19 in Victoria.
A surge in influenza and pneumococcus vaccinations was observed in 2020.
Process mining revealed insights contrasting with other geographical studies.
Abstract
Process mining is a discipline sitting between data mining and process science, whose goal is to provide theoretical methods and software tools to analyse process execution data, known as event logs. Although process mining was originally conceived to facilitate business process management activities, research studies have shown the benefit of leveraging process mining tools in different contexts, including healthcare. However, applying process mining tools to analyse healthcare process execution data is not straightforward. In this paper, we report the analysis of an event log recording more than 30 million events capturing the general practice healthcare processes of more than one million patients in Victoria--Australia--over five years. Our analysis allowed us to understand benefits and limitations of the state-of-the-art process mining techniques when dealing with highly variable…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Data Quality and Management · Machine Learning in Healthcare
