Type 2 diabetes (T2D) is associated with significant healthcare resource utilization, especially among patients with sub-optimal management, treatment-related adverse events including hypoglycemia, and comorbid health conditions. Value-based initiatives offer a unique solution to this problem, but additional evidence is needed to design and support these initiatives.

A Bayesian machine learning platform, Reverse Engineering Forward Simulation (REFS™), was applied to administrative claims data to identify predictors of key clinical and economic outcomes in T2D.

Machine learning models such as REFS have the potential to guide the provision of data-driven, individualized care with these results establishing the importance of ensuring that patients with T2D are appropriately treated with evidence-based interventions to ensure more favorable outcomes as well as control of healthcare resource utilization and costs.