Variance analysis in task-time matrix clinical pathways

H. Yan, P.M.E. Van Gorp, U. Kaymak, Lei Ji, X. Lu, C.C. Chiau, H. Korsten, Huilong Duan

Research output: Contribution to conferencePaperAcademic

452 Downloads (Pure)


Clinical pathways are popular healthcare management tools to standardise care and ensure quality. Measuring pathway conformance and analysing variances gives valuable feedback in the context of care improvement trajectories. The Business Process Model and Notation (BPMN) language and Task-Time matrices are popular ways to model clinical pathways. A key step in variance analysis involves the computation of optimal alignments between the pathway model and patient-specific traces. This paper presents for this step a new algorithm which reduces the time for finding deviations from hours to minutes. A case study on variance analysis is undertaken, where a clinical pathway from the practice and a large set of patients data from an EMR database are used. The results demonstrate that automated variance analysis between BPMN Task-Time models and real-life EMR data is feasible. We also provide meaningful insights for further improvement.
Original languageEnglish
Number of pages4
Publication statusPublished - 17 Feb 2017
EventInternational Conference on Biomedical and Health Informatics (BHI 2017), 16-19 February 2017, Orlando, Florida: Integrative Informatics for Precision and Preventive Medicine - Orlando, United States
Duration: 17 Feb 201719 Feb 2017
Conference number: 2017


ConferenceInternational Conference on Biomedical and Health Informatics (BHI 2017), 16-19 February 2017, Orlando, Florida
Abbreviated titleBHI 2017
Country/TerritoryUnited States
Internet address


Dive into the research topics of 'Variance analysis in task-time matrix clinical pathways'. Together they form a unique fingerprint.

Cite this