Insight

The Evidence Gap Is Not a Cardiology Problem

Only 8.5% of cardiology guidelines rest on solid randomized evidence. The rest lean on weaker data or expert opinion. And that number hasn't moved in a decade. This is the shape of clinical medicine.

A 2019 study in JAMA looked at ten years of cardiology guideline updates. The finding is striking.

8.5% of major cardiology recommendations rest on solid randomized evidence.

The remaining 90%+ lean on weaker data, observational studies, or expert opinion — and that proportion held steady across a full decade of updates.

Cardiology, it's worth noting, is one of the best-evidenced specialties in medicine. If anything, this is an optimistic picture of where clinical evidence stands.

Most of what we recommend to patients has never been rigorously tested — and the questions that matter pile up faster than we generate answers.

The bottleneck is not data. The data to answer many of these questions already exists — in electronic health records, in registries, in the datasets that accumulate quietly across health systems every day.

What is scarce is the capacity to turn that data into evidence: the methodology, the data work, the analysis infrastructure.

WHAT THE GAP COSTS US

  • A beneficial intervention that scales slowly because no one has demonstrated the benefit at scale.

  • A harmful signal sitting in a dataset no one had the resources to surface — while the intervention stays in place.

  • A question that simply never gets answered.

Powering a randomized controlled trial for every unanswered clinical question is not realistic. It never was. But we can do far better at leveraging the data that already exists.

Most of what we recommend to patients has never been rigorously tested — and the questions that matter pile up faster than we generate answers.

That is the problem Augura was built to solve.


About Augura
We built our platform around exactly this challenge: helping digital health companies and health systems design, execute, and communicate the causal evidence that turns a promising AI product into a credible, defensible clinical intervention.


Source: Fanaroff et al., Trends in Evidence Underlying Newly Approved Cardiovascular Drug Indications, JAMA 2019

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