When Dravida started researching RWD and RWE, he could not find a book on the subject with practical guidance on best practices on the use of RWD. He spent the last year working on this book and included other industry experts to create a complete and robust offering featuring regulatory guidance, best practices, case studies, and perspective from leaders in the pharmaceutical industry.
The discussion began with a simple definition and distinction made between RWD and RWE. RWD is defined as: Data that gets generated in routine clinical practice including physician’s offices, hospitals, in-home monitoring devices, pharmacies etc. including EMR/EHR, claims, patient and diseases registries, wearables data etc. RWE is the clinical evidence that is derived with the use of advanced analytics performed on the diverse datasets that make up RWD. To be successful in the use of RWE, Dravida notes that data scientists must have access to good data sources, valid statistical data, and sound insights. When leveraged correctly, RWE can provide a holistic view across pharma, including unmet needs of patients, disease prevalence and natural history of diseases, study feasibility, patient recruitment, innovative trial designs, medical affairs, market access, sales and marketing, and post- market surveillance.
Currently there are several challenges in implementing RWE including data access, data quality, and the lack of a standard data framework defined by regulatory agencies. With no templates and procedural mandates to collect this data, there are systemic issues as well and a need to convert all data to a standard data model.