Risk modeling, causal inference, and subgroup identification: three key statistical ingredients to effective learning health systems

Oregon Convention Center 

Learning health systems use internal data and experience to promote continuous improvement and innovation in health care delivery. Three key questions often underlie many tasks in building an effective learning health system: 'who is most in need of intervention?', 'what interventions work?' and 'what interventions work best for which individuals?'. In this talk we explore some examples of how statistical advances in risk prediction, causal inference, and subgroup identification can aid in these three key tasks. We provide an example of they have been used to help tailor intervention decisions in a large academic health system and discuss some key challenges to progress in a learning health system.