Translational Medicine - The Need for Integrative Informatics Amnon Shabo (Shvo) a,1 , Henning Müller b , Reinhold Haux c and Dipak Kalra d a IBM Research Lab in Haifa b University of Applied Sciences Western Switzerland, Business information systems c Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Germany d Centre for Health Informatics and Multiprofessional Education, University College London Abstract. Translational Medicine (TM) explores the barriers in translating innovations all the way from bench to policy by better translating results of studies in related disciplines, such as socio-economic, psychological and ethical studies. This panel discussion aims at exploring whether major paradigm changes redefine the known TM's barriers and what could be the role of integrative informatics in enabling interdisciplinary scalability needed to reach from bench to mainstream healthcare policy. Keywords. Translational Medicine, Integrative Informatics, Interdisciplinary Scalability 1. Introduction Translational Medicine (TM) is about the barriers in the way of biomedical discoveries to become accepted and useful knowledge utilized in healthcare. Current TM studies are conducted by multi-disciplinary teams of researchers capable of bringing basic biological discoveries to the clinical environment, and translate the results into new or revised clinical practice, informed by evidence from social, economic, psychological and other relevant sciences [1]. Developing new treatments towards the improvement of healthcare typically involves three translational barriers denoted "T1 - Bench to Bedside" where promising discoveries of biomedical research are tested in randomized controlled trials; "T2 - Bedside to Community" where bedside success stories are scaled up to work in a community; and "T3 - Community to Policy" where the new intervention becomes part of healthcare policies [2]. Some TM researchers also add the T4 barrier in the translation to population health. However, TM might perpetuate existing processes by looking for its predetermined barriers T1, T2, and T3, while it could be beneficial to also explore fundamental transformations in healthcare and then revisit these barriers. The hypothesis is that major transformations in healthcare could fundamentally change the barriers identified in TM and influence the TM research agenda (see examples in next section). 1 Corresponding Author email: shabo@il.ibm.com .