Context-based recommendation systems are increasingly and effectively used in various domains. Recommendation systems generate personalized, relevant recommendations for users. A critical problem recommendation systems face is the need for users to provide relevant contextual information actively, which may impact the recommendation system's ability to provide the best recommendations. Incorporating Internet of Things (IoT) technology presents an excellent opportunity to enrich the contextual data available to recommendation systems. The Internet of Medical Things has been raised; thousands of complicated sensors generate and broadcast data in the wireless sensor network; the data, in plans, is disseminated across the medical scheme. High-capacity hardware for data processing is needed to analyze and sort the data accurately in the cloud. In this paper, we discuss how advanced contextual information helps in mitigating data sparsity. How can we utilize data from IoT to support decision-making in prioritizing patients in an emergency? With the help of IoMT, we can collect much medical data from patients as they come to the hospital, thus enabling us to monitor their health and their medical conditions in real time. However, going through this huge and complex volume of data to prioritize patients during an emergency takes much work. This paper presents the development of an enhanced intelligent contextual recommendation system by using the Internet of Things, this system called ICRS_IoT. Therefore, as aim this paper focuses on enhancing the context-based recommendation system by using the Internet of Things to overcome data scarcity problems and by using IoT-based medical devices to collect medical data from patients as soon as they arrive at the hospital. We aim to handle hospital emergencies effectively using the data coming through from the Internet of Medical Things. The data collected by connected medical sensors enables informed medical decisions.