Tinnitus constitutes a highly distressing condition that impairs the quality of life of those affected. This problem highlights the urgent need to systematically optimize treatment and recovery. Objective: The aim of this work is to analyze and optimize the treatment process of tinnitus patients using process mining. In doing so, patient-reported mobile health (mHealth) data will be used to identify patterns in the course of treatment, in order to enable individualized and improved therapy approaches in the long term. Methods: Process mining techniques were applied to a data set of \(N=319\) tinnitus patients who collected mHealth data daily over a treatment period of 12 weeks. The event logs generated from these patient-reported data were processed using the process mining tool Apromore to model and visualize the course of the treatment processes. The analyses resulted in process models of stress values for patients with subjective improvement as well as for those with worsening of tinnitus. Results: The results show a possible correlation between stress and tinnitus. An increase in stress levels is linked to a worsening of symptoms, while a reduction in stress is associated with an improvement in tinnitus. Conclusion: The application of process mining to mHealth data provides valuable insights into the treatment trajectories of tinnitus patients.