Science is leading us down the path of increasingly targeted medicines with accompanying advances in genomics and our ability to develop personalised therapies for patients. However, there are increasing challenges in the development of drugs for ever smaller populations since, by definition, the rarer a disease is, the fewer people there are with the disorder that can participate in clinical research. EHDEN and other GDPR-compliant data-sharing platforms provide an alternative approach to drug development that enables researchers to search millions of historical anonymous patient records to model different scenarios and “health personas”, and to accelerate the development of medicines currently not available to patients in need. This session will provide an overview of RCT-alternative approaches, as well as explore the potential application of machine learning within the clinical trials domain.