Development, optimisation and implementation of artificial intelligence methods for real world data analyses in regulatory decision-making and health technology assessment along the product lifecycle
Real-world evidence derived from real-world data (RWD) has a promising role to inform regulatory decision-making. The use of RWD is established in regulatory processes such as safety monitoring, but evidentiary value for further use cases, especially in the pre-authorisation and evaluation phase of medicinal products, is rudimentary. The use of RWD in post-authorisation steps is constrained by data variability and by challenges in analysing data from different settings and sources. Thus, the development of new and optimised methods for RWD analyses is essential.
Based on highly relevant use cases from regulatory practice and across the product lifecycle, Real4Reg develops AI-based data-driven methods and tools for the assessment of medicinal products. Real4Reg addresses the challenges and opportunities of RWD analyses across different health care systems by involving multiple stakeholders to work together in a collaborative approach, reaching out also to already established European initiatives. The consortium assembles partners with outstanding excellence in the field of RWD analyses, including experts from regulatory agencies/HTA, academia and patient organisations. In an advisory board, stakeholders provide input and guidance to the project, including patients, industry, payers, HTA bodies and healthcare professionals. The structure and approach of the project facilitate the successful implementation of the effective use of RWD in regulatory decision-making and HTA, and ultimately supports the application of better medicines for patient. The findings will inform training activities on good practice examples and will be implemented in existing and emerging guidelines for both health regulatory authorities and health technology assessment bodies across Europe.
CSC provides the computing environment for developing AI models using sensitive medical data. CSC collaborates with the University of Eastern Finland (UEF) to implement use cases based on Finnish data which cannot be processed outside of Finland. CSC collaborates with Fraunhofer research organisation on AI model development.