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.

Factors Influencing Clinicians’ Use of Hospital Information Systems for Infection Prevention and Control: Cross-Sectional Study Based on the Extended DeLone and McLean Model (Preprint)

“…Hou et al confirmed that nurses using an electronic handover system can improve their communication efficiency [58]. Jalali et al discovered that information systems involving drug interactions can support physicians in making decisions more efficiently [59]. Literature evidence on the impact of satisfaction suggests that employees who are satisfied with a system are more likely to be more productive, especially if the use of such a system is mandated [60][61][62].…”

Object Type Clustering Using Markov Directly-Follow Multigraph in Object-Centric Process Mining

“…This graph is used to calculate a similarity matrix that enables the clustering of the case notions based on a threshold. Markov clustering is selected as it is widely applied in practice for different purposes, e.g., identifying protein-protein interaction networks [14], traffic state clustering [16], document clustering [11], and comparing similarities between different process variants [12]. To answer the second research question, a threshold tuning algorithm is defined to identify sets of different clusters that can be discovered based on all possible thresholds.…”

“…Although DFGs can be misleading due to lack of support for concurrency [22], they can be helpful as an intermediate model to discover more advanced models as done by, e.g., Split Miner [3], Heuristics Miner [25] and Fodina [24]. DFGs are also used in variant analysis where different models of a business process representing different variations can be compared to each other [12], [17], [18].…”