Real-World Evidence: Five Facts You Need to Know
Leveraging Real-World Data (RWD) is increasingly key for market access strategies and clinical research. Over the last 20 years, data gathered from electronic medical records, claims, medical devices, and the healthcare system has complemented clinical trial evidence—providing a clearer picture of the practice, outcomes, and costs of daily care. Now, RWD offers new possibilities to constitute synthetic control arms for clinical trials.
We asked Romain Finas, Vice President of Real-World Evidence (RWE) at Alira Health, to share five important facts about RWD and tell us about the future challenges of RWD for Europe.
1. Investing in multimodal data infrastructure is a must, especially in oncology
Right now, mixing genomics data, imaging, and electronic medical records is the gold standard for anyone stratifying populations by treatment response or developing personalized medicine algorithms in oncology. Meeting these current—and future—research demands requires sizable investments as the incorporation of “omics” data—tumor, circulating DNA, or Whole Genome Sequencing—supposes a significant upfront and long-term financial investment. It is not only a question of data collection and monitoring but also the capability to (re)run sequencing based on biobanks according to the latest techniques.
2. Non-fit-for-purpose data without a common data model applied in multiple centers
Beyond technical or data platform standards, it is essential to set common data models for each therapeutic area in collaboration with scientific associations, healthcare industries, and patient groups. This approach is key to focusing the collection efforts according to the targeted end-use of the data and providing relevant and qualitative sources. It also offers a real capability to develop multi-centric models reinforcing the value of the data for academic, institutions, or life science industries.
3. AI will facilitate massive data collection, curation, and deidentification
Thanks to AI development—particularly natural language processing—data processing can move beyond craftsmanship. A significant part of the investment to run a cohort must be allocated to train AI to “read” and “check” data plausibility and deidentify sources. This will support the Clinical Research Associate role in data monitoring and improve the capability to control larger datasets in terms of co-variates, volume of patients, or nature of files (bioinformatics, imaging, structured or unstructured data).
4. Public-private partnerships are key
Isolated sponsors (whether public or private) cannot succeed. The effort to design, collect, structure, enrich, or curate data demands long-term financing from full or permanent organizations. Public-private partnerships are now a true option to sustainably cover running costs. These partnerships also secure the generation of fit-for-purpose datasets that sponsors will pay for and help data sources stay ahead in the competitive global landscape of RWD-led research.
5. Strong patient engagement is essential
Clearly communicating the benefits of data usage to patients, as well as the massive investment it takes to transform raw data into regulatory and scientific-grade sources, is critical. Proper patient engagement and onboarding also avoids controversy around the monetization of personal data when it’s for the common good.
Alira Health is dedicated to the development of disease-centric data models and platforms co-designed by scientific associations, industry stakeholders, and patients. This represents a real opportunity to sustain Europe’s competitive advantages in the fierce global competition for health data.
At Alira Health, we are convinced that basing product access decisions on ethically collected, reliable data is a matter of sovereignty for healthcare institutions, especially in health technology assessment. It will also attract additional data research, offering patients earlier access to innovative health and care solutions.