The healthcare industry is witnessing a paradigm shift with the advent of Real-World Evidence (RWE) and Generative Artificial Intelligence (AI). These two concepts, when combined, have the potential to revolutionize healthcare delivery and patient outcomes.
Understanding Real-World Evidence
Real World Evidence refers to healthcare information derived from multiple sources outside of typical clinical research settings. This includes data from electronic medical records, claims and billing data, product and disease registries, and data gathered by personal devices and health applications. RWE helps to understand the effectiveness of a drug or tool to treat medical conditions in a real-world setting.
The Role of Generative AI in Healthcare
Generative AI refers to the application of AI techniques to generate new and original content relevant to healthcare. This could include medical images, personalized treatment plans, and more. Generative AI can analyze large volumes of medical data and create entirely new content. It can improve the quality of care, make it more accessible and affordable, reduce inequities in research and care delivery, and help companies unlock value in new ways.
The Intersection of RWE and Generative AI
The intersection of RWE and Generative AI in healthcare is a promising field. Generative AI can take unstructured data sets—information that has not been organized according to a preset model, making it difficult to analyze—and analyze them. These unstructured data sets can be used independently or combined with large, structured data sets, such as insurance claims.
Potential Applications and Benefits
Generative AI has potential use cases across the healthcare industry. For example, it can automate tedious and error-prone operational work, bring years of clinical data to a clinician’s fingertips in seconds, and modernize health systems infrastructure. Moreover, RWE bridges the gap between theory and practice. It harnesses data from everyday sources such as EHRs, wearables, registries, lab reports, etc., and analyzes it to check what works for patients and what doesn’t.
