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Discover how Savana
extracts and analyzes clinical data using AI

Deep RWE.

SAVANA CAN GET SO MUCH DEEPER INTO THE INFORMATION

Thanks to NLP, Deep RWE Incorporates richer clinical characteristics, like outcomes and biomarkers, into high-validity registries and pragmatic trials.

This offers an opportunity to understand populations across clinical criteria while providing high visibility into cohorts of interest, interventions, and clinical and health-economics outcomes.

By engaging health systems directly, we go to the source for the highest quality phenotype data.

Moving literally from hundreds of variables into tens of thousands of them, allow us to query the databases with disruptive questions.

Benefits.

TAKING ADVANTAGE OF DATA EXTRACTED USING AI

Descriptives Studies and Outcomes  benefits:

  • Less time consuming for PIs.
  • Minimize selection bias if we have all available data (all patients per site).
  • It prevents follow up bias (all departments per site).
  • Unlimited variables (under request).

From hundred to tens of thousands of variables and Predictive Modelling benefits:

  • Explore correlations / associations.
  • Generate and test predictive models.
  • Create new hypothesis.

Savana’s clinical NLP external validation.

01. Generate a gold standard:

A set of EHRs manually reviewed by clinical researchers from all hospitals participating in the research study.

– Compared with Savana’s automatic NLP.

02. Compare Savana’s identification:

Versus the experience of real doctors (gold standard).

Deep RWE basically incorporates richer clinical characteristics into high-validity virtual registries.

That offers an opportunity to understand populations across clinical criteria while also supporting care pathway enhancement.

It is possible to pragmatically achieve high visibility into cohorts of interest, interventions, and clinical and financial outcomes.

By engaging health systems directly, we go to the source for the highest quality phenotype data.

Moving literally from hundreds of variables into tens of thousands of them, allows us to query the databases with disruptive questions about biomarkers.

Want to use it?:

Start with your proposed AI + RWE use case:

This is the first step for AI + RWE: