Clinical evidence derived from the analysis of Real-World Data (RWD), such as electronic health records, medical claims, and patient-generated data, used to evaluate the safety and effectiveness of AI models in routine clinical practice.
Data collected from everyday patient care (like electronic health records) used to prove an AI tool actually works and is safe in the real world, not just in a highly controlled, artificial lab experiment.
In healthcare AI, RWE is increasingly required by regulators (e.g., the FDA, EMA) for the post-market surveillance of AI/ML-based Software as a Medical Device (SaMD). It is used to monitor model drift, validate ongoing clinical effectiveness, and ensure long-term patient safety across diverse, uncontrolled patient populations outside of rigid, traditional clinical trials.
RWE is critical for lifecycle management and reimbursement. Payers and health systems increasingly demand RWE to justify paying for AI tools, proving they deliver actual economic and clinical value outside of highly controlled trial environments. It is also the primary mechanism for satisfying regulatory post-market surveillance requirements.
An AI model approved to predict sepsis is deployed in 50 hospitals. Over two years, the developer collects Real-World Evidence from the hospitals’ EHRs to prove the model maintains its accuracy across different patient populations and doesn’t suffer from alert fatigue, satisfying the FDA’s post-market monitoring requirements.