Discovering health-care processes using DeciClareMiner

Flexible, human-centric and knowledge-intensive processes occur in many service industries and are prominent in the health-care sector. Knowledge workers (e.g., doctors or other health-care personnel) are given the flexibility to address each process instance (i.e., episode of care) in the way that...

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Bibliographic Details
Published in:Health systems 2018-09, Vol.7 (3), p.195-211
Main Authors: Mertens, Steven, Gailly, Frederik, Poels, Geert
Format: Article
Language:English
Subjects:
decision mining
health-care modelling
knowledge extraction
Process mining
Special Issue: SIG-Health Design, Innovation, and Impact of Healthcare IT
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Summary:Flexible, human-centric and knowledge-intensive processes occur in many service industries and are prominent in the health-care sector. Knowledge workers (e.g., doctors or other health-care personnel) are given the flexibility to address each process instance (i.e., episode of care) in the way that they deem most suitable. As a result, the knowledge of these processes is generally of a tacit nature, with many stakeholders lacking a clear view of a process. In this paper, we propose an algorithm called DeciClareMiner that combines process and decision mining to extract a process model and the corresponding knowledge from past executions of these processes. The algorithm was evaluated by applying it to a realistic health-care case and comparing the results to a complete search benchmark. In a relatively short time (10 min), DeciClareMiner was able to produce a DeciClare model that represents 93% of episodes of care with atomic constraints. Compared to the 50 h required to calculate the 100%-episode model via an exhaustive search approach, our result is considered a major improvement.
ISSN:2047-6965
2047-6973
DOI:10.1080/20476965.2017.1405876