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• W2754961800 abstract "Clinical pathways (CPs) are popular healthcare management tools to standardize care and ensure quality. Analyzing CP compliance levels and variances is known to be useful for training and CP redesign purposes. Flexible semantics of the business process model and notation (BPMN) language has been shown to be useful for the modeling and analysis of complex protocols. However, in practical cases one may want to exploit that CPs often have the form of task-time matrices. This paper presents a new method parsing complex BPMN models and aligning traces to the models heuristically. A case study on variance analysis is undertaken, where a CP from the practice and two large sets of patients data from an electronic medical record (EMR) database are used. The results demonstrate that automated variance analysis between BPMN task-time models and real-life EMR data are feasible, whereas that was not the case for the existing analysis techniques. We also provide meaningful insights for further improvement." @default.
• W2754961800 created "2017-09-25" @default.
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• W2754961800 date "2018-03-01" @default.
• W2754961800 modified "2023-10-17" @default.
• W2754961800 title "Aligning Event Logs to Task-Time Matrix Clinical Pathways in BPMN for Variance Analysis" @default.
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• W2754961800 doi "https://doi.org/10.1109/jbhi.2017.2753827" @default.
• W2754961800 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28922133" @default.
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