Advances in technology and computing power have made real-world data (RWD) ubiquitous in healthcare. The evidence generated from this data (real-world evidence; RWE) is increasingly used in healthcare decision-making, although its use is not yet optimized. Sponsors are also using RWD internally to inform R&D and commercialization activities. While the focus is on increasing and optimizing the current use of RWD/RWE in decision-making, the field is exploring innovative ways to solve barriers to evidence generation and expand use cases for RWD. For example, natural language processing (NLP) is enabling broader use of RWD by extracting meaningful information buried in text fields. Synthetic data enhances privacy and amplifies existing datasets to potentially correct bias and increase sample size. Applying artificial intelligence (AI) for drug repurposing is an emerging use case that can increase efficiency in drug discovery.

This forum will focus on three areas of innovations that have the potential to make a paradigm shift in how RWD/RWE is used:

1. NLP
2. Synthetic data
3. AI for drug repurposing

The forum will start with a discussion on NLP, which is now an accepted way to mine text data. The experience of NLP can provide a roadmap for faster adoption of other innovations. In addition, new NLP techniques will be explored. The forum will also discuss the role that synthetic data can play in RWE generation and propose ideas on how to overcome the current challenges to adoption. Finally, the forum will discuss using RWD to identify novel drug signals, and how this can improve efficiency in drug development. The speakers will present case studies and outline how these innovations can improve healthcare decision-making.