Imagine trying to sell a high-tech medical device, but the insurance companies and governments can't agree on a fair price because it's too new. This project creates a digital calculator that helps everyone agree on a price based on the actual benefit to the patient over time. It's like a smart pricing guide that updates as more data about the medicine's success becomes available.
Innovative medicines and devices are often too expensive for health systems to afford, leading to long delays in patient access. Current pricing methods fail to account for the evolving value of a technology over its entire life cycle.
A cloud-based Policy Support Tool containing pricing models, value assessment models, and reimbursement schemes implemented in R.
If you are a biotech firm dealing with high development costs and uncertain pricing for rare diseases — this project developed pricing and value assessment models that help set affordable prices while rewarding innovation. This ensures your therapy reaches patients faster by reducing pricing disputes.
If you are a manufacturer dealing with slow reimbursement approvals for high-risk devices — this project developed a Policy Support Tool that simulates different payment schemes. This allows you to predict budget impacts and justify the cost to health authorities.
If you are a payer dealing with the high cost of precision cancer medicines — this project developed dynamic cost-effectiveness models that update as clinical data changes. This prevents overpaying for technologies that don't deliver the expected long-term results.
The project develops new pricing models designed to balance patient access with the need to encourage entrepreneurship. Based on available project data, these models aim to set affordable prices through a value-based approach.
Yes, the tools are specifically tested for three use cases: precision cancer medicine, cell- and gene therapy, and high-risk medical devices (Class IIb, III, or IVD Class D).
Based on available project data, the Policy Support Tool will be publicly available and hosted on a cloud-based platform following open science principles.
It uses dynamic models that allow for updates when there are changes in medical indications or when new comparators enter the market.
The project is currently in the development and testing phase, with working versions implemented in R for integration into the final cloud platform.
The consortium is well-balanced for commercial translation, featuring a 30% industry ratio with 3 industrial partners and 4 SMEs. With 10 partners across 7 European countries, the group combines academic research from 3 universities with practical application from 4 other entities, ensuring the tool is grounded in both theory and market reality.
Erasmus Universiteit Rotterdam
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