  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 processes and large
data-sets. While we provide solutions to the identified limitations, the
overarching goal of this study was to detect differences between the patients`
health services utilization pattern observed in 2020--during the COVID-19
pandemic and mandatory lock-downs --and the one observed in the prior four
years, 2016 to 2019. By using a combination of process mining techniques and
traditional data mining, we were able to demonstrate that vaccinations in
Victoria did not drop drastically--as other interactions did. On the contrary,
we observed a surge of influenza and pneumococcus vaccinations in 2020,
contradicting research findings of similar studies conducted in different
geographical areas.
