The adoption of the Internet of Things (IoT) in healthcare has received considerable interest in the past decade. Indeed, IoT-based solutions are poised to transform how we keep people safe and healthy especially as the demand for solutions to lower healthcare costs increases in the coming years. However, the heterogeneity of the things that can be connected in such environments makes interoperability among them a challenging problem. Moreover, the observations produced by these things are made available with various vocabularies and data formats. This heterogeneity prevents generic solutions from being adopted on a global scale and makes difficult to share and reuse data for other purposes than those for which they were initially set up. In this book chapter, we provide an overview of the different solutions from both technical and semantic perspectives that have been used recently to tackle the interoperability issue in such IoT environments and especially in healthcare domain. We also present an overview of semantic middleware solutions that have combined the technical and semantic techniques for a complete interoperable solution.

BACKGROUND Communication and interpersonal skills for general practitioners in medical consultations play an important role in providing high-quality health care, and in establishing good doctor-patient relationships. Serious games are a good vector to enforce simulation exercises to develop effective communication strategies. In such games the learner plays the role of a health care provider and the patient is simulated by a computer program. OBJECTIVE The purpose of this study is to develop a dialogue engine that can simulate a doctor-patient dialogue during a consultation encounter process. METHODS We define a model of the medical consultation process and of the patient profile and use ontologies to feed a dialog engine RESULTS The proposed model is called the medical consultation process model (MCPM) and is implemented with an ontology called MSCOnto (Medical Consultation Session Ontology) and described in the Web Ontology Language (OWL) syntax. We explain the architecture and workflow of the dialogue engine and show how the engine can be set up to define different types of patient profiles, and to generate conversations depending on doctor choices. CONCLUSIONS The model allows the representation of the processes and actors of a medical consultation. Processes are break down into phases, micro-sequences, dialogues session and phrases. Patient profiles and exchanged information can also be expressed. Tests have shown that the presented work lays the foundation of a system able to generate a realistic dialogue between a doctor and a patient.