Recently, the internet of things (IoT) technologies plays a very important role in various important sectors such as healthcare, education and industry. It has changed the conventional way of diagnosing some diseases and accelerating the check-up process through using IoT medical devices. Many IoT devices are available for measuring the biomarkers such as heart rate, sugar level and blood pressure, etc. However, the privacy of these data collected via IoT medical devices remains a challenge that hinders the use of these devices in clinical practice. The massive data collected by these devices vary in their sensitivity to patients. The more sensitive data requires fast computation and processing to avoid any delay that may occur. The processing of such data in the cloud may lead to operations delay which is needed by a real-time monitoring application. Therefore, this research intends to provide Healthcare Internet of Thing (H-IoT)-based framework for the classification of streamed data according to their criticality level. After the classification, the more crucial data will be computed in fog rather than in the cloud to avoid latency. Future work will extend this work by implementing the proposed framework and evaluating its outcomes.