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.
The limitations of open claims in audience models
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:
- Longitudinal continuity
- Comprehensive visibility into treatment journeys
- Confirmation that a condition is actively treated versus loosely suspected
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.
Why inference fails for rare and complex conditions
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:
- Over-inclusion of patients who do not meet true clinical criteria
- Dilution of audience quality that obscures performance signals
For rare and specialty conditions, the consequences are more severe:
- Small errors in modeling translate into massive percentage distortion
- Audiences become statistically fragile
- Measurement results lose credibility with clinical, regulatory, and executive stakeholders
In both cases, teams are left asking the same questions:
- Are these patients actually diagnosed or just statistically likely?
- Are we measuring real behavior change or noise created by inference?
- Would these insights hold up under scrutiny?
Claims are a proxy for care, not a complete record
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.
Rethinking what trustworthy audiences require
Trustworthy healthcare audiences are not built from a single dataset. Most importantly, they must be designed to support measurement, not just activation. They require:
- Multiple, complementary data sources (open claims, closed claims, pharmacy, and more)
- Deterministic identity resolution instead of probabilistic matching
- Clinically grounded signals rather than demographic or behavioral proxies
"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
Where HealthVerity Audiences are fundamentally different
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:
- Open and closed medical claims
- Pharmacy data
- Longitudinal linkage across sources
- Deterministic identity resolution governed by privacy-first controls
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.
