Over the last 10 years, Artificial Intelligence (AI) healthcare foundation models have made tremendous strides when it comes to delivering highly accurate predictive results when compared to traditional models. This is especially true for models developed and trained on the ‘language’ of healthcare. When trained on healthcare data sequences (claims, EHR, CPT, ICD, pharmacy, lab, etc.), these models deliver far more accurate results as to WHAT beneficiary-level health events will occur and even WHEN they will occur. Via a discussion between Jason Schumacher, Premera Blue Cross Director of Underwriting, and Dean Noble-Tolla, Chief Product and Analytics Officer for Prealize Health, session attendees will hear how these types of foundation models are leveraged in practice today at large health plans like Premera and the benefits that these health plans are enjoying in use cases such as stop-loss underwriting. Attendees will understand the capabilities of AI-driven predictive analytic solutions, the nuances of implementing these models within existing enterprise practices, as well as the advantages of rapid task-specific model development, reduced model training times, and most importantly, predictive accuracy.