Real World Evidence

AI/ML-enabled real-world evidence analytics

Augment value for pharma and medical devices by analyzing real-world evidence (RWE) across the product life cycle.

Real-world evidence

As the focus on value-based pricing increased, the importance of RWE rises to support clinical inferences on treatment efficacies. The information from electronic health records (EHR) and electronic medical records (EMR) along with other patient treatment records is compelling to guide companies, physicians, and the managed care ecosystem to deliver truly benefitting patient outcomes. Razor-sharp data and analytics capabilities are crucial for harvesting real-world data (RWD) and delivering evidence-based assessments across the healthcare value continuum.

Axtria’s experience with RWD processing and management, along with its AI/ML RWE models help healthcare stakeholders generate and demonstrate value and meet growing evidence generation requirements.

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Read Report / HEOR value demonstration and evidence generation challenges

AI-driven solution applications

Drivers for evidence-based demonstrations across the product life cycle

Pre-launch

Launch and scale

Loss-of-exclusivity (LOE)

Pre-launch

Unmet needs and disease burden
Patient recruitment
Understand the standard of care
Clinical trial design

Launch & scale

Patient safety
Product comparative effectiveness
Patient adherence
Prescribing patterns
Differentiation in sub-populations
Long-term clinical outcomes
Target population implications

Loss-of-exclusivity (LOE)

Effects of switching on outcomes
Usage differences
Differentiate with or vs. protected formulation

For payers to deliver cost-effective care coverage and value for patients’ money.
For providers to identify better ways to manage patient health.
For life sciences organizations to make better decisions related to pre-launch, market access, and commercialization.

Read Blog / Implications from HEOR and RWE models for biopharmaceutical commercial analytics