Adaptive Visualization of Health Information Based on Cognitive Psychology: Scenarios, Concepts, and Research Opportunities

Consumer Health Information Systems (CHISs) are indispensable in healthcare. User-centered evidence-based medical information for patients positively influences therapy success, behavior, and cause–effect comprehension. Also, improved health literacy allows patients to accept medical advice and share decision-making and improves doctor–patient communication. Today, CHISs exist in many different forms. Yet, information is generally provided statically, i.e., the same medical content is presented to everyone. However, patients vary regarding previous knowledge and information needs and preference of perception of the information, e.g., in textual or visual form. This variation can depend, e.g., on gender, age, personality, perception, and cognitive aspects.

In this conceptual chapter, we envision how research and knowledge from evidence-based medical knowledge, cognitive-psychological mechanisms, and interactive data visualizations can be combined, to form adaptive and interactive consumer health information systems (CHISs) that take account of individual health information needs and increase health literacy by providing a reliable source of medical knowledge. To this end, we detail the scope and contributions of these disciplines to novel visual health information systems which can adapt them to the information needs and preferences of their consumers. We depict a concept for an advanced interactive, adaptive, personalized visual CHIS (named A
CHIS). The concept is based on introducing multidimensional adaptivity in the content, visual presentation, level of detail, for example, to the provision of evidence-based medical health information, aiming at the consumers’ full understanding of the meaning of the provided medical content. We argue that adaptive visual health information may provide efficiency increase for the general medical system and improved health literacy. While we do not present concrete results, we lay out the research opportunities and a possible system architecture to inform and implement ACHIS in the future.