Discovering health-care processes using DeciClareMiner. Issue 3 (2nd September 2018)

• Record Type: Journal Article
• Authors: Mertens, Steven; Gailly, Frederik; Poels, Geert
• Is Part Of: Health systems. Volume 7:Issue 3(2018)
• Page Start: 195
• Page End: 211
• Publication Date: 2018-09-02
• Subjects: Process mining -- decision mining -- health-care modelling -- knowledge extraction; 610.285
• DOI: 10.1080/20476965.2017.1405876
• Languages: English
• ISSNs: 2047-6965

Abstract

Abstract: 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.