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

BMC Medical Informatics and Decision Making

Paul Johannesson¹  Amin Jalali¹  Erik Perjons¹  Ylva Askfors²  Anikó Vég²  Tero Shemeikka²  Abdolazim Rezaei Kalladj² 

[1] Department of Computer and Systems Sciences (DSV), Stockholm University;Health and Medical Care Administration, Region Stockholm;

关键词: Process variant analysis;    Process mining;    Markov chain;   

DOI  :  10.1186/s12911-021-01715-3

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown