Rare diseases create a unique challenge for evidence generation. The number of patients is often small, diseases are heterogeneous, and clinical knowledge can be limited. Traditional clinical trials may struggle to recruit enough participants, follow patients for a sufficient period of time, or capture the complexity of disease progression in real life. For these… Continue reading Rare Disease Registries: How Much Data Is Enough? Balancing Scientific Rigor with Clinical Reality
Tag: RWE
From Wearables to Real-World Evidence: Are We Ready for Consumer-Generated Health Data?
The recent partnership between Oura and Eli Lilly has raised considerable attention across the healthcare industry. At first glance, it looks like another collaboration between a technology company and a pharmaceutical manufacturer. However, looking deeper, it signals a broader transformation that could introduce new aspects on how patients are monitored throughout their treatment journey. … Continue reading From Wearables to Real-World Evidence: Are We Ready for Consumer-Generated Health Data?
The Practical Applications of AI in Real-World Evidence Studies
Artificial intelligence has become a very frequently discussed topic also in real-world evidence (RWE) research, registries, and clinical data science. Conferences, publications, and industry reports routinely highlight AI as a transformative force capable of accelerating evidence generation, improving data quality, and expanding the utility of healthcare data. However, much of this discussion remains high… Continue reading The Practical Applications of AI in Real-World Evidence Studies
From Feasibility to Architecture: Building a Dual-Purpose Data Strategy for RWE and AI
Following the previous discussion on how data feasibility differs between fit-for-purpose RWE studies and fit-for-training AI systems, and the technical characteristics that support each assessment, the next question is a practical one: how should organizations actually build a data strategy that can support both? Because once you determine that the same dataset must serve… Continue reading From Feasibility to Architecture: Building a Dual-Purpose Data Strategy for RWE and AI
Data feasibility in Healthcare: Comparing fit-for-purpose and fit-for-training
Healthcare organizations are investing heavily in both real-world evidence (RWE) studies and artificial intelligence (AI). At first glance, these two fields seem to rely on the same raw material: large healthcare datasets drawn from electronic health records (EHRs), claims databases, registries, genomics, imaging, and patient-generated data. Because the source data often overlaps, it is tempting… Continue reading Data feasibility in Healthcare: Comparing fit-for-purpose and fit-for-training