  Within Process mining, discovery techniques had made it possible to construct
business process models automatically from event logs. However, results often
do not achieve the balance between model complexity and its fitting accuracy,
so there is a need for manual model adjusting. The paper presents an approach
to process mining providing semi-automatic support to model optimization based
on the combined assessment of the model complexity and fitness. To balance
between the two ingredients, a model simplification approach is proposed, which
essentially abstracts the raw model at the desired granularity. Additionally,
we introduce a concept of meta-states, a cycle collapsing in the model, which
can potentially simplify the model and interpret it. We aim to demonstrate the
capabilities of the technological solution using three datasets from different
applications in the healthcare domain. They are remote monitoring process for
patients with arterial hypertension and workflows of healthcare workers during
the COVID-19 pandemic. A case study also investigates the use of various
complexity measures and different ways of solution application providing
insights on better practices in improving interpretability and
complexity/fitness balance in process models.
