MMP: A hybrid AI approach for explainable diagnosis of concept drifts in patient pathways

Business processes in complex and dynamic environments, such as hospitals, are subject to constant changes. This could arise from adaptation to unforeseen events, creating uncertainty and inefficiency in clinical pathways. Traditionally grounded in sub-symbolic AI, Process Mining provides insights into process behavior through actions such as concept drift analysis, including detection , characterization , and explainability . However, most existing studies focus on drift detection, making it challenging to identify the root causes of process deviations. The literature indicates that this limitation largely stems from the difficulty of integrating domain-specific knowledge into process mining. This article targets this issue and presents the Minuscule Movement of business Processes (MMP) approach to diagnose drifts and deviations in patients’ pathways through two main steps. First, it defines a meta-model to embed domain knowledge such as potential causes of drifts into process discovery analyses and to generate artificial traces that simulate process deviations. In the second step, these traces are assessed using the proposed ProDIST algorithm to find the process most similar to the discovered workflow. The identified process is then used to diagnose and determine the root causes of the drift. MMP’s practical applicability is assessed through a real-life experiment in healthcare. Accordingly, MMP successfully detected process drifts in the healthcare case study, achieving consistent accuracy and providing interpretable insights that enabled domain experts to identify potential causes of process drifts and deviations. • Automated root-cause diagnosis of business processes within Process Mining, showcasing a comprehensive hybrid AI approach called MMP. • Demonstrating explainability in process mining. • A platform to integrate healthcare domain knowledge with the location data of patients. • Augmenting process mining analyses through embedding domain knowledge to explain causes of concept drifts. • Introducing the ProDIST algorithm to measure the distance between business processes.