Towards the Second-Order Adaptation in the Next Generation Remote Patient Management Systems

Remote Patient Management (RPM) systems are expected to be increasingly important for chronic disease management as they facilitate monitoring vital signs of patients at their home, alerting the care givers in case of worsening. They also provide patients with educational content. RPM systems collect a lot of (different types of) data about patients, providing an opportunity for personalizing information services. In our recent work we highlighted the importance of using available information for personalization and presented a possible next generation RPM system that enables personalization of educational content and its delivery to patients. We introduced a generic methodology for personalization and emphasized the role of knowledge discovery (KDD). In this paper we focus on the necessity of the second-order adaptation mechanisms in the RPM systems to address the challenge of continuous on-line (re)learning of actionable patterns from the patient data.

eHealth Personalization in the Next Generation RPM Systems

In this work we present a possible next generation Remote patient management (RPM) system that enables personalization of educational content and its delivery to patients and introduce a generic methodology for personalization emphasizing the role of knowledge discovery. 1