Enabling Large-Scale Telemedical Disease Management through Interactive Visualization Dominique Brodbeck a , Roland Gasser b , Markus Degen a , Serge Reichlin b,c , Jürg Luthiger a a University of Applied Sciences Northwest Switzerland, Olten, Switzerland b Medgate Telemedical Center, Basel, Switzerland c University Hospital Basel, Department of Internal Medicine, Basel, Switzerland Abstract Automated collection and storage of medical data leads to large amounts of heterogeneous and time-dependent information. Out of this follows the problem of how to access and interpret this data, in order to support therapeutic decision making. Telemedical disease management holds great potential for the efficient and effective treatment of chronic diseases. The realization of this potential however depends on finding a solution to the information overload problem. This paper describes a highly interactive visualization system that gives caregivers an overview of trends and critical patterns, and provides easy access to details without loosing the big picture. We report on the results from two case studies that confirm the validity of this approach and suggest that it is well suited to enable large-scale telemedical disease management programs. Keywords: Disease Management; Telemedicine; Decision Support Systems, Clinical; User-Computer Interface 1. Introduction Advances in information technology have greatly improved our abilities to collect and administrate large amounts of data. Medical data can be brought together from many sources and stored in central databases. The fact that data is available however, does not automatically imply that it is also useable. Standard database interfaces for instance employ a “drill-down” way of navigation and data access that typically only shows isolated aspects of the data and make it difficult to get the big picture. While there are many initiatives whose concerns are the standardisation, security, and reliability of medical data, we find little efforts that support the decision- and sense-making part of the process. In this paper we look at telemedical disease management as an example for this situation. Disease management is an approach to the treatment of chronically ill patients [1], consisting of a disease-specific system of decision making algorithms, coordinated healthcare interventions and communications for populations with conditions in which patient self-care efforts are significantly involved [2]. Applying telemedical techniques to disease management has the potential to help transfer the knowledge and practice of positive health behaviour to patients, and make the management of their condition more effective and more