Health records and patient generated data (in health forums or social media) constitute what the FDA and the CDC refer to as "real world data", which can be extremely valuable and become ‘real world evidence’ to advance health research. However, using these data presents many challenges for large-scale studies, as it is sometimes misused.

In this talk, I will showcase some of the approaches my team has deployed to identify cohorts, reduce data bias, find what drives patients to switch medications, and enrich metadata for SARS-CoV-2 sequences in public repositories, among other projects that incorporate RWD using natural language processing and artificial intelligence techniques.