Walgreens prioritizes tackling barriers to medication access—such as cost and prior authorizations—believing that adherence can only be addressed once a patient can consistently obtain their therapy. This frames the two issues as a sequence, not parallel challenges.
The effectiveness of AI and machine learning models for predicting patient behavior hinges entirely on the quality of the underlying real-world data. Walgreens emphasizes its investment in data synthesis and validation as the non-negotiable prerequisite for generating actionable insights.
By analyzing real-world data with machine learning, Walgreens can identify patients at risk of non-adherence before a clinical issue arises. This allows for early, personalized interventions, moving beyond simply reacting to missed doses or therapy drop-offs.
With over 9 million daily interactions across nearly 8,000 stores, Walgreens' vast physical footprint is the primary engine generating the real-world data that powers its analytics for pharma partners. Its brick-and-mortar scale is its core data advantage.