United States healthcare is embroiled in a crisis of inconsistent quality and overwhelming cost. Through two administrations, the national healthcare strategy has focused on using data and technology to control costs and improve care. Congress has enacted legislation to encourage measurement of quality, sharing of data, payment based on quality of care, and transparency within the healthcare system. While these goals are bipartisan and lofty, implementation requires both hard work from health systems and robust technology. Industry has developed technologies that incrementally further the national agenda, including electronic health records, computer assisted coding, and population health analytics. Each of these supports workflow within the healthcare system and improves profit margin for healthcare organizations. But, approaches that go beyond workflow, using data to better understand clinical care, are lacking. With newly available electronic health data and a massive increase in processing power, data-driven personalized medicine is just now becoming possible. It will require advanced semantic technologies to understand clinical care strategies that have been tried in the past, but that have unknown efficacy. It will pose informatics challenges in inferring inclusion criteria, interventions, and outcomes from incomplete and poorly structured data. It will require deep clinical understanding to run real-time pragmatic clinical trials based on real world data to understand complex patients. The goal, dependent on Phase I success, is to create the first commercial system to support healthcare in running real-time pragmatic clinical trials using full clinical data. This will augment the standard of care defined by randomized controlled trials to actually tailor therapy for those complex patients that account for the majority of healthcare spends, but for whom complexity precludes tailored randomized trials.