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.
Related Insights
The majority of what payers identify as 'care gaps' are actually 'data gaps'—a lack of information leads to an assumption of missing care. By solving the data acquisition problem first, organizations can distinguish between the two. This dramatically shrinks the problem set, focusing expensive outreach efforts only on patients with true care needs.
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.
By negotiating prices down from over $1,000 to as low as $150 per month, the government deal fundamentally shifts Ozempic's market position. It is no longer a high-end luxury akin to plastic surgery but an accessible wellness product comparable to a fancy gym membership, dramatically expanding its addressable market.
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.
The friction of navigating insurance and pharmacies is so high that chronic disease patients often give up, skipping tests or medications and directly worsening their health. AI can automate these tedious tasks, removing the barriers that lead to non-compliance and poor health outcomes.
Eli Lilly and Novo Nordisk's DTC programs for weight loss drugs give employers an alternative to point employees towards, providing cover to drop expensive insurance coverage and potentially reducing access for patients who rely on it.
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.
Chronic disease patients face a cascade of interconnected problems: pre-authorizations, pharmacy stockouts, and incomprehensible insurance rules. AI's potential lies in acting as an intelligent agent to navigate this complex, fragmented system on behalf of the patient, reducing waste and improving outcomes.
The large gap between insulin's list and net price was driven by Pharmacy Benefit Managers (PBMs). Their business model, which takes a percentage of the rebate, incentivized pharma to raise list prices to offer bigger discounts.
The core issue preventing a patient-centric system is not a lack of technological capability but a fundamental misalignment of incentives and a deep-seated lack of trust between payers and providers. Until the data exists to change incentives, technological solutions will have limited impact.
