Discovering health-care processes using DeciClareMiner

• 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
• ISSN: 2047-6965; 2047-6973
• DOI: 10.1080/20476965.2017.1405876

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.