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Patients suffering from multiple diseases (multi-morbid patients) often havecomplex clinical pathways. They are diagnosed and treated by differentspecialties and undergo other clinical actions related to various diagnoses.Coordination of care for these patients is often challenging, and it would beof great benefit to get better insight into how the clinical pathways developin reality. Discovering these pathways using traditional process miningtechniques and standard event logs may be difficult because the patient isinvolved in several highly independent clinical processes. Our objective is toexplore the potential of analyzing these pathways using an event logrepresentation reflecting the independent clinical processes. Our main researchquestion is: How can we identify valuable insights by using a multi-entityevent data representation for clinical pathways of multi-morbid patients? Ourmethod was built on the idea to represent multiple entities in event logs asevent graphs. The MIMIC-III data-set was used to evaluate the feasibility ofthis approach. Several clinical entities were identified and then mapped intoan event graph. Finally, multi-entity directly follows graphs were discoveredby querying the event graph visualizing them. Our result shows that pathsinvolving multiple entities include traditional process mining concepts not forone clinical process but all involved processes. In addition, the relationshipbetween activities of different clinical processes, which was not recognizablein traditional models, is visible in the event graph representation.

Discovering care pathways for multi-morbid patients using event graphs