dfgcompare: a library to support process variant analysis through Markov models

Abstract

Background: Data-driven process analysis is an important area that relies on software support. Process variant analysis is a sort of analysis technique in which analysts compare executed process variants, a.k.a. process cohorts. This comparison can help to identify insights for improving processes. There are a few software supports to enable process cohort comparison based on the frequencies of process activities and performance metrics. These metrics are effective in cohort analysis, but they cannot support cohort comparison based on the probability of transitions among states, which is an important enabler for cohort analysis in healthcare.

Results: This paper defines an approach to compare process cohorts using Markov models. The approach is formalized, and it is implemented as an open-source python library, named dfgcompare. This library can be used by other researchers to compare process cohorts. The implementation is also used to compare caregivers' behavior when prescribing drugs in the Stockholm Region. The result shows that the approach enables the comparison of process cohorts in practice.

Conclusions: We conclude that dfgcompare supports identifying differences among process cohorts.

Keywords: Markov chain; Process mining; Process variant analysis.