KA-Recsys: Patient Focused Knowledge Appropriate Health Recommender System.

Patients with chronic diseases, such as diabetes, cancer, and heart disease, actively participate in disease management and seek health information on a constant basis for decision-making and self management. Patient focused health recommender systems (PHRSs) that suggest health information relevant to patients' changing information needs across their disease trajectory can provide significant help to patients as they manage their disease on a day-to-day basis. A unique requirement of the PHRS would be to suggest health information in line with patients' changing knowledge about the disease. It is crucial to recognize that patient knowledge of the disease may change as they become more actively involved in understanding and self-managing the illness. By providing patients with appropriate information, they are more likely to not only understand and engage, but also learn. Hence, the purpose of this doctoral thesis is to explore technologies in the field of recommender systems and personalized learning for the purpose of suggesting health information that accounts for patients' dynamic information needs and level of knowledge about disease. We will explore these ideas in the context of developing a knowledge-appropriate PHRS ( KA-Recsys). As a case study, a recommender will be integrated to an existing ovarian cancer patients' information access portal. To assess the utility of KA-Recsys, the system will be evaluated based on expertbased and patient-based feedback. The expectation is that health information suggested by KA-Recsys will increase as well as benefit patients' involvement in self management and treatment decisions.