###### Abstract  
In modern telemedicine systems, the data path can be exceedingly complex, with data passing through a number of phases and processes before reaching its ultimate state. It is extremely difficult to predict the effects of lacking data or improperly processed data on the final outcome. In real-time systems, and particularly in the field of telemedicine, it is crucial to rapidly identify and rectify issues in order to prevent the loss of large amounts of data and the degradation of data quality. A basic simulation is insufficient for a comprehensive examination of the system; instead, modeling approaches are required. However, a minor system's state space can be immense. We present a methodology and a hybrid framework that incorporate simulation, emulation, and modeling in order to evaluate the state space and potential consequences of a sufficiently large system in a more targeted and condensed manner. In this paper, we demonstrate the structure and operation of our framework using an actively researched telemedicine use case, as well as how data quality can fluctuate and new anomalies can emerge if data is corrupted during an intermediate phase. According to our real-time Atrial Fibrillation (AF) and classification use case, data loss can be as high as 15%. data quality; modeling; simulation; real-time systems; telemedicine +
Footnote †: journal: Medical Imaging and Simulation in Real-Time Telemedicine Systems. data path, the higher the possibility and extent of data corruption. We have implemented a distributed system model in Temporary Language of Actions (TLA) in order to prove that latency can significantly influence the consistency and the quality of data. After the verification of the elaborated formal specification, we found that the data quality materially deteriorates as latency increases. Furthermore, it is also proved that, in a perfect environment in which no latency is present, we still cannot guarantee 100% data quality for a distributed system. Based on this, we have examined how the data quality of a system can be affected by varying the latency. In paper [2], we modeled a distributed telemedicine system and ran simulations with different parameters. After examining the state space, we observed that such systems can be described with Directed Acyclic Graphs (DAGs), which is a special structure that eases the analyses and the predictions. These results motivated us to model a practical use case in which data quality plays an important role and can have serious implications in case of significant data quality reduction. Internet of Things (IoT), 5G, Artificial Intelligence (AI), Big Data and other advanced technologies present a huge opportunity for telemedicine [3]. The 5G-enabled IoT and AI are continuously bringing innovative applications in telemedicine. IoT and 5G technologies are already present in telesurgery where real-time capability is a requirement [4]. Real-time ElectroCardioGram (ECG) monitoring is a frequently applied technique and beneficial in atrial fibrillation (AF) detection. Nowadays, real-time ECG monitoring can be carried out easily using lightweight wearable sensors but there are also procedures in hospitals in which practitioners pay serious attention to the AF detection. In addition, there are use cases that require real-time ECG monitoring and immediate evaluation where buffering and late decision making are not allowed [5]. In such situations, it is important to be able to continuously provide data to the algorithm, so that it processes them as quickly as possible and does not cause congestion. However, mostly the final results cannot be obtained with a single aggregational step because another level of aggregation is required that uses the results of the first level. It is covered in many studies [6; 7] that AF detection itself cannot be used for specifying a treatment. Further analysis has to be made in order to find the severity of the symptoms.