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.

A patient's self-reported data can be incomplete or biased, as they may only report the "good measures." To get the full picture, companies must gather input from multiple sources, like caregivers and clinicians. Each perspective helps correct the others, creating a more accurate and holistic view of the patient's journey.

Electronic Health Record (EHR) companies have historically used proprietary formats to lock in customers. AI's ability to read and translate unstructured data from any source effectively breaks these data silos, finally making patient data truly portable.

We possess millions of data points on interventions, but they are useless to AI models because they're trapped in thousands of disparate EMRs in varied formats. The challenge is not generating more data, but solving the human incentive and alignment problems required to create unified data registries.

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.

Unlike controlled clinical trial data, real-world evidence is derived from vast, messy, and incomplete data from daily healthcare. This variability is its strength, offering deeper insights into long-term outcomes, drug interactions, and diverse patient populations that clean trial data misses.

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.

To be effective, the patient's lived experience cannot remain a "soft narrative." It must be converted into hard data points—like reduced healthcare utilization for payers or influence on treatment pathways for clinicians—to become a decision-making tool they cannot ignore.

Existing healthcare datasets like claims are flawed proxies. The most valuable, untapped data will come from new sources like AI scribe encounters and patient-uploaded data into modern PHRs. This 'ground truth' data will unlock novel applications like usage-based malpractice insurance.