Healthcare marketing has reached an inflection point and for years, success was defined by reach: how many impressions were delivered, how efficiently audiences could be scaled, how quickly campaigns could be launched. To achieve that scale, much of the industry turned to what was easiest to access: open medical claims and modeled audiences built on top of them.
But as healthcare data has grown more complex, and as the questions marketers are trying to answer have become more clinically specific, that approach is starting to falter and presently the challenge becomes the accuracy of the targeting.
Many healthcare marketing platforms rely primarily, even exclusively, on open medical claims to define patient and provider audiences. While this seems sufficient because claims are standardized, broadly available and relatively easy to scale, they tell an incomplete story.
Open claims often lack:
As a result, many vendors compensate by layering in modeled assumptions which means relying on non-clinical attributes, proxy behaviors, or probabilistic signals to “guess” the condition of a population. That may work directionally for broad, high-prevalence diseases but it breaks down quickly in less prevalent diseases or conditions with complex diagnosis journeys and pathways of care.
Using non-clinical attributes to infer condition populations introduces compounding error and the problem isn’t limited to rare disease.
For broad conditions, inference leads to:
For rare and specialty conditions, the consequences are more severe:
In both cases, teams are left asking the same questions:
Medical claims, especially open claims, were never designed to be a complete representation of patient reality. They reflect potential billing activity but not care in the full context.
Without closed claims, pharmacy data, lab signals, and other complementary sources, marketers are forced to fill gaps with assumptions. The more assumptions you introduce, the further you move from the reality of the patient journey which skews downstream analysis.
Trustworthy healthcare audiences are not built from a single dataset. Most importantly, they must be designed to support measurement, not just activation. They require:
"When healthcare audiences are inferred, insight breaks down. Modeled assumptions dilute accuracy, obscure performance, and weaken confidence. Here’s why truth-based, clinically grounded data matters more than scale."
- Richard Pisciotta, Media and Consumer Manager at HealthVerity
HealthVerity Audiences are built on the belief that healthcare insight requires clinical truth built from real-world data.
Instead of relying primarily on open claims and inference, HealthVerity brings together:
This approach reduces reliance on non-clinical proxies and modeled guesswork, enabling audiences that reflect real patient and provider populations, across any therapeutic area including any rare diseases.
Discover how HealthVerity Audiences use clinically grounded, multi-source data to deliver insight you can stand behind.