The cancer care pathway comprises several stages encompassing diagnosis, treatment, and follow-up. Studies show that delays in treatment initiation are associated with worse outcomes, including increased mortality, reduced progression-free survival, and diminished post-treatment quality of life. To address this, patient navigation tools have emerged as a strategy to identify bottlenecks and mitigate delays. In this context, process mining offers a promising approach to discover, model, and optimize workflows using real data from hospital information systems. This paper presents a case study on the application of process mining to analyze care pathways in an oncology clinic. The focus was on identifying critical pathways and delays in the treatment journey to support the patient navigation program. Based on the insights gained, targeted improvement actions were proposed to enhance the patient journey. Using the PM2 methodology, event data were extracted and processed from the clinic’s information systems to model and analyze two key processes: (i) departmental workflows related to ambulatory care and (ii) longitudinal treatment pathways from initial evaluation to discharge. The results confirm the value of process mining for improving oncology patient journey and highlight its potential as a decision-support tool for healthcare administrators and clinical leaders.
Santos et al. (Thu,) studied this question.