Process management is essential for improving the performance and quality of organizational processes, especially in healthcare, where processes are characterized by high complexity and variability. These challenges become even more problematic in Emergency Departments, where operations involve time-critical decisions and diverse treatment paths. In this study, Process Mining was applied, following an adapted datascience lifecycle, to compare the theoretical process model defined by a Brazilian private hospital with the actual patient flow recorded in its system, assessing compliance through the fitness metric and examining factors that explain deviations, such as age, diagnosis, and severity of the case. The results show that although 1/3 of patients follow the expected sequence of steps, path deviations occur among more severe cases, which exhibit longer and more complex trajectories. Additionally, system inconsistencies also contribute to some of the divergences observed in relation to the theoretical model. The study concludes that Process Mining provides a clear view of real patient flows, revealing how clinical characteristics and data quality influence process adherence and highlighting opportunities for operational improvement in emergency departments.