Computational Methods for Next Generation Health Care Next generation healthcare will be driven by prevention and treatment strategies that take individual variability into consideration. Much of this variability is captured in the large amount of data of different types that has become available: clinical encounters, lab results, diagnostics, medications, genomics, and increasingly, physiological, lifestyle, social behavioral and environmental data. The challenge is how to leverage modern methodologies from machine learning, data mining, and decision science to extract insights from all this data collected over large populations, in order to apply them at individual level to improve health and wellness outcomes. The overarching goal of Computational Health Research is to enable this journey from complex and diverse health data to useful insights for individuals. At IBM Research we have been systematically developing advanced artificial intelligence and data science methodologies for healthcare, ranging from intelligent data preparation and pattern extraction, to complex models for actionable insights generation, to delivery and engagement optimization. These methodologies have been applied to a wide range of use cases and disease areas. I will discuss these methods and use cases, lessons learned and important future directions.