Process mining in healthcare using control flow perspective: A case study

The main operations in hospitals are trying to maintain their process in the progressive manner that is healthcare sectors are try to streamline their processes. In order to do so, it is essential to have an accurate view of the care flows under consideration. In this paper, we apply process mining techniques to obtain meaningful knowledge about these flows, e.g., to discover typical paths followed by particular groups of symptoms that create a disease of a patient. This is a non-trivial task given the dynamic nature of healthcare processes. The paper demonstrates the applicability of process mining using a real case of a patient affected by a disease in a hospital. Using a variety of process mining techniques, we analyzed the healthcare process using three different algorithms (1) α-algorithm (2) Heuristics Miner algorithm and (3) Damped Working Set (DWS) algorithm. In order to do so we extracted relevant event logs from the hospital information system and analyzed these logs using the ProM framework. This paper only deals about control flow perspective of the healthcare processes. Therefore the results show that process mining can be used to provide new insights that facilitate the improvement of existing treatment system.