Improving the matching process of dental care recommendation systems by using subjective criteria for both patients and dentists

Abstract

Nowadays, healthcare recommendation systems are matching health professionals with patients based on preferences such as location, type of treatments, price, availability or other information including their type of health insurance. In the health social network domain, subjective criteria such as attitude, personality and behaviour have not been considered for matching of patients and health professionals. In this research, we focus on dental care recommendation systems and we aim at introducing subjective criteria in the matching process. Patients are profiled in terms of attitudes, personalities and behaviours through a set of questionnaires, derived from the popular methods such as DISC (Dominant, Influencer, Steady, and Compliant) personality test. In addition, we use crowdsourcing to extract feedback from patients and to profile dentists according to their qualities (e.g.: Friendly, caring, rude, etc.). These qualities are then used in the matching process. A thorough investigation on how to improve the matching process of a patient's subjective profile with a dentist's qualities is done through online questionnaires and focus group. The research aims at deriving a dynamic set of matching rules to improve the process of recommendation that includes subjective aspects so that in the future, patients can be better matched with the 'right' dentist for them