## 5 Experimental Study  
### Datasets  
We consider two cases of process model discovery in the current study, on which the proposed solution was applied and validated. The first process to discover is remote monitoring of patients suffering from arterial hypertension provided by PMT Online ([[https://pmtonline.ru/](https://pmtonline.ru/)]([https://pmtonline.ru/](https://pmtonline.ru/)) (in Russian), accessed on 12 November 2022) (a company specializing in the development of medical information systems and telemedicine systems). The second process is the daily activities of medical personnel in the Almazov center ([[http://www.almazovcentre.ru/?lang=en](http://www.almazovcentre.ru/?lang=en)]([http://www.almazovcentre.ru/?lang=en](http://www.almazovcentre.ru/?lang=en)), accessed on 12 November 2022) in Saint Petersburg, one of the leading cardiological centers in Russia. These cases in healthcare seem to be much better for exploring complexity because healthcare processes are highly diverse and uncertain on multiple levels of implementation. We present a summary of all datasets in Table 2 and give a detailed description below. First, we applied the proposed discovery technique to the monitoring event log consisting of 35,611 events, 272 cases corresponding to different patients, and 18 types of activities performed by operators, physicians, and nurses during monitoring of patients with arterial hypertension all over Russia within a telemedical system developed by PMT Online. We combined activity labels with corresponding resources to reveal additional role interactions. If the same activities performed by different workers are aggregated, it is similar to clustering events, which are highly correlated in context sense. The remote monitoring program for patients with hypertension is as follows: the patients measure their blood pressure at home on a regular basis, and each record made by a toolkit is transferred to a server, where data are then processed. There are several clinical events for  
Figure 7: UML class diagram for the main classes of ProFIT library. medical staff that measurements may trigger. The main ones are \"Red zone\" and \"Yellow zone\" that signify exceeding critical (emergency instance) and target (urgent instance) levels of blood pressure, respectively. These events have to be processed by operators and doctors, which may take some actions according to a scenario, e.g., contacting a patient by appointment or instantly. Usually, the \"Red zone\" events occur for patients that do not have an appropriate treatment plan yet. When the health state is normalized with medications, the \"Yellow zone\" appears as opposed to a \"Red zone\", or it is possible for the patient to be transferred to a therapy control program to maintain blood pressure levels. There are also non-clinical events such as \"New med. program\" when a patient is registered for remote care, \"Meas. missing\" when data are not received by the server, etc. Ideally, when target levels are achieved, and the kit is returned to the monitoring provider (as well as events), the program comes to an end with the \"Monitoring completed\" event. A more challenging case study is discovering regular daily activities (workflow) of doctors and nurses from a not-process-aware hospital information system. Our colleagues from the Almazov National Medical Research Centre provided us with an anonymized database with patient electronic health records covering COVID-19 treatment cases in their facility from March 2020 to June 2021. The dataset is a collection of fragmented medical records from patient histories including patient id, event id, event description, and associated record section name, timestamp, specialist name and type, department, record status, and supplementary information as a semi-structured text. We created an event log from the raw data source following event log imperfection patterns [21]. They were form-based event capture, distorted label, collateral events, homonymous label, etc.