Using Process Mining to Connect Process Orientation and Data-driven Decision Making in Healthcare: a Qualitative Assessment and Integration of New Data Sources

Abstract

Healthcare organizations face increasing pressure to deliver care that is not only efficient and cost-effective, but also patient-centered and responsive. In this context, process orientation (PO) and data-driven decision making (DDDM) are widely promoted as complementary paradigms to improve healthcare delivery. However, their integration in daily practice remains fragmented. While PO fosters end-to-end thinking across organizational silos, DDDM relies on the growing availability of healthcare data to support operational and clinical decisions. The central aim of this doctoral research is to strengthen the connection between PO and DDDM by enriching process insights with experiential and engagement-related dimensions of care. Process mining bridges both approaches by analyzing real-world care pathways. Yet, most applications only focus on execution data, which limits the scope of how the patient experienced the process. This doctoral research explores how non-traditional data sources, specifically remote health monitoring and patient-reported experience data can be integrated into process mining analyses. In doing so, the research identifies key methodological, technical, and organizational challenges that arise when extending process mining beyond its conventional data foundations. The research is structured around three interrelated studies: (1) a qualitative study of Flemish hospital departments to assess the current state, opportunities and challenges of integrating PO and DDDM; (2) a process mining study using remote monitoring data in a cardiology context; and (3) a process mining study at a hospital that combines event logs with patient experience data in a breast cancer care pathway. Together, these studies aim to advance both the conceptual understanding of process mining as a means to integrate PO and DDDM, and its methodological application in data-rich, patient-centered healthcare environments.