Unveiling Hidden Patterns in Flexible Medical Treatment Processes - A Process Mining Case Study

摘要

In hospital environments, treatment processes, resp. clinical pathways, are adopted based on the health state of a patient. Modeling of pathways is time consuming and due to the involvement of many participants, the introduction of clinical pathways is cost-intensive. Process mining offers a possibility for automatic or semi-automatic creation of clinical pathways based on the event log data recorded during the process execution in hospital information systems. However, state-of-the-art algorithms struggle to discover meaningful end-to-end patterns from highly flexible clinical log data. This challenge can be addressed by Local Process Models. They allow pathways to be modeled partially, thus enabling the detection of major process steps. In our case study, we apply this recently proposed method on a real world clinical dataset and discuss results and challenges.