Process Mining for Demographic Insights: A Subpopulation Analysis in Healthcare Pathways

Demographic variations in healthcare pathways are key for delivering effective and equitable patient care. Examining pathway differences across age and gender groups can help uncover demographic-specific disparities in care delivery. In this paper, we demonstrate the use of the Process Mining Project Methodology in Health-care (PM2HC) for the subpopulation-based analysis of treatment pathways, using process mining techniques. We validate this methodology through a case study on frozen shoulder treatment using the MIMIC-IV data set. Key findings reveal distinct procedural sequences for male and female patients, as well as notable age-based variations in treatment choices and timelines. These insights underscore the influence of demographic factors on healthcare processes. Expert evaluations further highlight the practicality of the methodology and its potential to guide targeted interventions that address various patient needs, thus enhancing personalized care. This work contributes t o clinical research and practice by identifying inefficiencies and informing tailored interventions. Future efforts will extend the methodology to other medical conditions and integrate multi-institutional data for broader applicability. By advancing process mining in healthcare, this research provides insight into improving patient care and addressing demographic diversity.