The objective of this project is to accelerate implementation of advanced predictive analytics (e.g., machine learning and deep learning) in clinical diabetes care. Near-term outcomes are predicted, with 1-2 new outcomes added cumulatively each calendar year. Current models predict 90-day rise in HbA1c and 180-day risk for hospital admission for diabetic ketoacidosis. Low-risk health care delivery interventions are deployed using quality improvement methods. Higher risk or novel interventions with unknown efficacy are deployed by embedding pilot clinical trials into Plan-Do-Study-Act cycles in the broader quality improvement projects. Up to 9 PDSA cycles with 4-9 novel interventions will be executed per year. Adoption packages will be created to guide other diabetes centers on implementing predictive analytics with novel interventions via quality improvement-based approaches.