Imagine if doctors' handwritten notes and medical images were automatically turned into a neat spreadsheet. This tool does exactly that, turning messy hospital records into clear data. It helps health authorities decide if a new cancer drug is actually worth its price based on how patients do in real life, not just in controlled trials.
Regulators and health authorities struggle to assess the true cost-effectiveness of new cancer drugs because hospital data is often unstructured and trapped in medical notes or images.
An AI-based system to structure medical notes and images, and structured data entry modules for EMR systems.
If you are a drug developer dealing with slow regulatory approvals and pricing disputes — this project developed an AI-based system that transforms unstructured medical notes into structured evidence. This allows you to prove the real-life effectiveness of your therapies to HTA bodies more quickly.
If you are an EMR provider dealing with fragmented data entry in oncology — this project developed structured data entry modules for modern electronic medical records. This improves the quality of data collected during standard clinical routines.
If you are a hospital network dealing with high treatment costs and unsustainable budgets — this project developed an end-to-end infrastructure for reporting real-world data. This helps you implement value-based care to reduce the financial burden of cancer treatment.
The project aims to increase cost-effectiveness and sustainability of cancer care by providing data-driven evidence for regulatory and HTA decision-making.
Yes, the project involves 13 partners across 6 countries, focusing on building data collection capabilities in leading European cancer hospitals.
Based on available project data, the specific licensing terms are not mentioned, but the project focuses on providing an end-to-end infrastructure and guidelines.
The project specifically addresses legal constraints in cancer hospitals to ensure secure and legal access to real-world data.
The project period runs from 2022-12-01 to 2026-11-30, indicating it is currently in the implementation and validation phase.
The consortium is heavily industry-weighted with a 54% industry ratio, comprising 7 industrial partners and 2 SMEs. This strong commercial presence, combined with 3 research entities and 3 other organizations across 6 countries, suggests a high focus on practical application and market integration rather than pure academic research.
Contact HUS-YHTYMA in Finland for partnership opportunities regarding RWD infrastructure.
Contact us to find out how to integrate these AI-based RWE tools into your clinical pipeline.
