The Power of Causal Machine Learning

Results that are explainable, unbiased and actionable

There are a myriad of artificial intelligence (AI) technologies making their way into healthcare. As a result, there’s a lot of confusion about the best AI technology to solve different types of problems.

As a pioneer in machine learning and its application to healthcare, GNS brings an unparalleled depth and breadth of experience in leveraging AI to solve healthcare’s most crucial problems. And as the availability and variety of data continues to grow, our AI continues to grow smarter.

Our technology is unique in the field. REFS (Reverse Engineering Forward Simulation) is a causal machine learning platform – an extremely powerful form of AI that learns directly from the data. Unlike other AI technologies, that rely on scanning and interpreting available data, REFS discovers new insights from the data. This is an important distinction as the results from causal machine learning are objective, unbiased and actionable.

REFS starts by transforming trillions of data variables – clinical, genetic, lab, image, drug, genomic, proteomic, consumer, pharmacy and unlimited other types – into causal models, models that demonstrate cause and effect. Once the models are built, causal relationships are calculated by running hundreds of thousands of “what if” simulations through the models to illuminate the underlying relationships, so critical to treating patients precisely. Biology is complex and unless you understand the “why” or root cause, it’s difficult to intervene or treat patients successfully.

As the data continues to grow, REFS continues to grow smarter and provide additional discoveries. This allows our clients to discover new drugs, unravel disease pathways, identify patients and biomarkers that are responsive to specific treatments, leverage real world evidence and data to prove the value of drugs, identify optimal care pathways and inflections points for patients, and much more.

Healthcare’s Most Powerful AI Solution

Understand the power of causal machine learning, why it’s different than other AI solutions and why it’s critical for healthcare.

When we succeed, we’ll close the gap between data and action for healthcare

Because when we reveal healthcare as a complex system of causal relationships, there’s no limit to how much more effective organizations everywhere can become.


What patterns and variations of medical practice drive the greatest value and best health outcomes?


How will this drug fare in a trial – and why?


How do I prove that my drug is superior to the competition’s, using real world data?


What’s the right care to administer to specific patients to reduce hospital stays?


What combination of treatments does this patient need now?


Which individuals would most benefit from this drug?

The Power of Causal Machine Learning

Condenses years of research into months

Improving health outcomes, saving billions of dollars and speeding drug discovery and development

Discoveries are hypothesis-free

REFS works on data discoveries, providing clean, unbiased actionable insights


Empowers value-based decisions based on real world evidence

By providing scientific evidence found in real-world data, healthcare decisions can be made that deliver the best value and most cost-effective solution at the patient and population levels at the right inflection points

Advanced machine learning with transparent cause and effect discoveries

The REFS platform identifies the underlying cause and effect relationships within the data, no “black box”


Enables precision medicine

By matching the right treatment to the right patient at the right time better health and financial outcomes are realized

Data Agnostic

Transforms millions of data points of all types and sizes to calculate causal relationships that drive outcomes