Abstarct: Healthcare environments are trying to provide timely and high-quality medical services. At the same time, due to the dynamc, unstructured and multidisciplinary nature of healthcare processes, these processes are not easily understood, like treatment and diagnosis in healthcare environments. The treatment path for each patient is different according to the patient's conditions and the department that is in charge of the patient's treatment. Each treatment department has its own applications. One of the problems of examining existing processes in health and treatment centers and collecting information is the variety of applications. These problems in therapeutic environments are assumed problems in this research. In this research, we use the data available in the hospital information system that have been created over time and process analysis techniques to analyze the processes related to hospitalized patients. Process mining means discovering and improving real processes using data extracted from event reports in the hospital information system. Identifying and discovering processes in therapeutic environments is the first step in their analysis and improvement. Its advantages include reducing service costs, increasing process transparency, reducing patient waiting time, and improving resource efficiency. The innovation of this research is the use of the event report registered in the hospital information system instead of the simulation and questionnaire method and the creation of a new dataset of hospital events. The evaluation of the results obtained with the recorded event report is more accurate than the simulation and the traditional questionnaire method. The purpose of this research is the application of process analysis and its techniques to identify processes in therapeutic environments. For this purpose, the analysis of processes related to hospitalized patients is studied. In order to improve the performance of the process of admission to discharge of patients, we discover the best process model and identify the deviation and bottleneck of event reporting from the discovered model. The algorithm used to discover the process model is a heuristic