The conversation among healthcare stakeholders – biopharma, payers, providers and consumers- is shifting to one centered on value. The increasing pressure to demonstrate the effectiveness of a drug or therapy is driving value-based contracts, risk sharing agreements and new strategies towards proving value.
The availability of real-world data and adoption of powerful machine learning technology is providing biopharma the ability to uncover novel insights and produce the real-world evidence (RWE) to support value-based programs and conduct evidence-based conversations with payers.
REFS, our powerful causal machine learning platform works by transforming millions of data points — including EMR, claims, genetics, proteomics, labs, imaging, genomic, molecular and an unlimited number of others — to identify patient subpopulations who respond to drugs and model disease. Evidence generated helps biopharma identify specific therapeutic offerings, generate Efficacy to Effectiveness evidence by simulating clinical trial results with real world data, pursue label expansion, and support value-based contracting.
Leveraging AI to Answer Questions Across the Entire Drug Lifecycle
Our value based drug models powered by REFS answers crucial business questions across the entire drug lifecycle, from clinical trials to launch support to post launch.
- Can we identify subpopulations of patients with unmet needs?
- Can we identify patients likely to be responders? Can that help inform future trials?
- Can we identify subpopulations of patients that will accelerate and reduce costs of clinical trials?
- Can we predict real world outcomes for a drug still in trial and begin to demonstrate real world value?
- Which products are right for value-based contracts?
- How do you define the right health outcomes to prove success?
- Can we model patients likely to initiate or discontinue our drug?
- Can we identify the right subpopulation of patients for whom you are willing to go “at risk”?
- Can we predict rapidly progressing patients who are likely to have poor outcomes if not treated differently from standard of care?
- Will a treatment work for additional indications?
- Can we identify a subpopulation of patients that should receive the drug as a first line therapy vs. second line?
- Can we show improved clinical and economic outcomes for patients who move the drug to first line?
- Will a treatment improve health outcomes if given earlier or in combination with standard of care?
- Can we optimize outcomes based on drug choice? Or drug combined with “beyond the pill” initiatives?
Value-based Drug Models
Leveraging powerful AI and real world data to generate evidence that addresses changing market dynamics
Leveraging proven disease models in a variety of therapeutic areas – including oncology, immune-oncology, rheumatoid arthritis, diabetes, NASH, migraines, multiple sclerosis and others – biopharma can run real world data through the models to understand which subpopulations respond to as drug, identify patients that should receive the drug as a first or second line of therapy, demonstrate economic outcomes for moving a drug to first line, identify additional indications for a treatment, and understand if a drug will improve health outcomes if given earlier or in combination with a standard of care. By gaining insights into specific drugs with RWE, biopharma can prove value, conduct evidenced based conversations with payers, and expand the label and elongate patents of current drugs.
Leveraging Real World Data
The key to successful implementation of value-based initiatives lies in biopharma’s ability to make the data collected in the real world actionable. By leveraging real world data and AI, biopharma can generate a broad set of insights about a drug’s performance in the real world prior to launch, define a product strategy and value proposition earlier in the clinical trial phase, and demonstrate market differentiation during a drug’s growth phase.
REFS Platform Licensing
For biopharma companies who would like to build, simulate and interpret their own models, they can license our cloud-based REFS platform to accelerate drug discovery and development or uncover insights into current drugs. GNS data scientists and machine learning experts are also available to identify targeted goals, develop customized models, build and perform simulations, conduct subpopulation analysis and drug target identifications as well as develop reports, abstracts and presentations.