Introducing patient and dentist profiling and crowdsourcing to improve trust in dental care recommendation systems

Abstract

Healthcare blogs, podcasts, search engines and health social networks are now widely used, and referred as crowdsources, to share information such as opinions, side effects, medication and types of therapies. Although attitudes and perceptions of the users play a vital role on how they create, share, retrieve and utilise the information for their own or recommend to others, recommendation systems have not taken the attitudes and perceptions into considerations for matching. Our research aims at defining a trust dependent framework to design recommendation system that uses profiling and social networks in dental care. This paper focuses on trust derived in direct interaction between a patient and a dentist from subjective characteristics’ point of view. It highlights that attitudes, behaviours and perception of both patients and dentists are important social elements, which enhance trust and improve the matching process between them. This study forms a basis for our profile-based framework for dynamic dental care recommendation systems.