Comparative Process Mining for Identifying the Critical Activities in Sepsis Trajectories

Sepsis, a life-threatening condition with high mortality and readmission rates, demands precise and timely management to improve patient outcomes. Despite advancements, identifying the critical activities within sepsis treatment pathways remains a challenge, limiting the effectiveness of interventions. This study addresses this issue by utilising comparative process mining techniques to analyse sepsis trajectories, focusing on key performance metrics-sojourn time, arrival rate and finish rate-across distinct patient clusters. The analysis is based on real-life event logs from a hospital's sepsis cases, using K-means clustering to segment patients by age, severity and key clinical indicators. The study reveals critical activities such as 'Return ER', 'Admission IC', and 'Release C', which consistently exhibit high sojourn times and influence patient outcomes significantly. These activities emerge as bottlenecks in the patient care process, particularly in cases of severe sepsis, where delays can lead to increased complications and mortality. By identifying these critical points, the study provides actionable insights for healthcare providers to optimize resource allocation, reduce delays and enhance the overall efficiency of sepsis management. The findings underscore the importance of targeted interventions in these key areas, offering a path toward improved clinical outcomes and reduced sepsis-related mortality and readmission rates. This research contributes to the growing field of process mining in healthcare, highlighting its potential to transform complex clinical pathways into more efficient and effective treatment processes.