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
Category: Data
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
From Variable Assessment to Model Readiness: Technical Differences in Data Feasibility for RWE and AI
In my previous article, I discussed how healthcare datasets might be suitable for real-world evidence (RWE) studies, artificial intelligence (AI) applications, or both. The overlap between these use cases is real, but the technical process used to evaluate a dataset is often very different. For RWE, many professionals are familiar with structured frameworks for… Continue reading From Variable Assessment to Model Readiness: Technical Differences in Data Feasibility 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
How EMA Is Redefining Credibility in Real-World Evidence
Why Governance May Become the New Competitive Advantage in RWE The publication of the European Medicines Agency’s new Data Quality Framework for EU medicine regulation: application to Real-World Data (RWD) marks an important moment in the evolution of Real-World Evidence (RWE) research in Europe. While much of the discussion around RWE has traditionally focused… Continue reading How EMA Is Redefining Credibility in Real-World Evidence