Abstract
Processes in organisations such as hospitals, may deviate from intended standard processes, due to unforeseeable events and the complexity of the organisation. For hospitals, the knowledge of actual patient streams for patient populations (e.g., severe or non-severe cases) is important for quality control and improvement. Process discovery from event data in electronic health records can shed light on the patient flows, but their comparison for different populations is cumbersome and time-consuming. In this paper, we present an approach for the automatic comparison of process models extracted from events in electronic health records. Concretely, we propose to compare processes for different patient populations by cross-log conformance checking, and standard graph similarity measures obtained from the directed graph underlying the process model. Results from a case study on breast cancer care show that average fitness and precision of cross-log conformance checks provide good indications of process similarity and therefore can guide the direction of further investigation for process improvement.
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Notes
- 1.
promtools.org last accessed 2019-05-06.
- 2.
https://networkx.github.io/, last accessed 2019-05-20.
- 3.
Process models for the other populations were omitted due to space constraints.
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This work was supported by the Hospital Group Twente (ZGT) by providing data, secure server infrastructure and domain advice.
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Marazza, F. et al. (2019). Comparing Process Models for Patient Populations: Application in Breast Cancer Care. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_40
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