Descriptive Process Mining of Pulmonary Clinical Pathways Before and During COVID-19

Understanding how clinical pathways evolve over time is essential for characterizing care processes. It also helps identify potential shifts in diagnostic and organizational practices. This study provides a descriptive analysis of patient trajectories for four major respiratory conditions: lung cancer, interstitial fibrosis, chronic obstructive pulmonary disease (COPD), and pneumonia. Trajectories were compared between a pre-COVID-19 period (2018–2019) and a COVID-19 period (2020–2022) in a specialized hospital. Using process mining applied to administrative event logs, we examined three aspects of care: the structure and sequencing of activities, the timing of transitions between care encounters, and imaging timeliness. The analysis spanned inpatient, emergency department, and outpatient settings. Indicators of care duration and transition timing revealed heterogeneous temporal patterns. Several conditions showed shorter intervals in the COVID-19 period, whereas others varied little. Activity-level analyses complemented these findings. Process maps indicated stable structural components in many pathways, together with differences in timing and execution. In the emergency department, care shifted toward bedside radiography, whereas CT chest volumes remained relatively stable across periods and settings. Imaging timeliness stayed consistently high in the emergency department and relatively stable for most inpatient conditions. Outcome-related indicators, including 30-day readmission and prolonged care trajectories, showed only modest differences between periods. Overall, the study demonstrates the value of process mining for describing real-world clinical pathways and identifying temporal variations in care. These results provide a foundation for future work that integrates richer clinical information and analytical approaches capable of assessing causal relationships.