EntityRisk provides the PROVEN™ software platform, which integrates health economics and policy expertise with machine learning to model health outcomes and substantiate product value. This platform allows biopharma companies to simultaneously test multiple value frameworks, quantify evidence impact, and optimize target product profiles. The solution delivers robust disease models, pricing validation, and evidence quantification for defensible value stories in a dynamic system.
Funding
$2.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
The biopharmaceutical industry faces challenges in accurately assessing the real-world value and risks associated with new healthcare technologies, leading to uncertainty in drug pricing, market access, and evidence generation strategies. Traditional cost-effectiveness analysis often fails to capture the full spectrum of social value and may not adequately address the needs of diverse patient populations.
Solution
EntityRisk offers the PROVEN™ platform, a machine-learning-enabled software solution that quantifies the real-world value of new healthcare technologies and identifies associated risks. The platform leverages clinical trial data, real-world data, financial data, and published estimates to generate probabilistic predictions of outcomes and value. PROVEN™ automates generalized cost-effectiveness analysis, enabling users to evaluate value from multiple perspectives and quantify both upside opportunities and downside risks. By integrating advanced mathematical simulation modeling, the platform forecasts revenues and cash flows under various contracting structures and accounting rules, providing practical commercial insights for pricing and market access decisions.
Target Audience
The primary target audience includes clinical and commercial-stage biopharma companies, investors, and NGOs involved in the development, evaluation, and commercialization of new healthcare technologies.
Features
- Bayesian machine learning models for probabilistic predictions of real-world outcomes
- Automated generalized cost-effectiveness analysis (GCEA) from multiple perspectives
- Microsimulation engine for forecasting revenues and cash flows under various scenarios
- ValueML™ module for simulating value-based prices and cost-effectiveness outcomes
- CommercialML™ module for supporting go-to-market pricing and value-based care strategies
- EvidenceML™ module for simulating commercial opportunities and risks from evidence-generation strategies
- PriceML™ module for predicting real-world net prices for target product profiles
- OutcomesML™ module for forecasting the distribution of real-world clinical effectiveness