The growing availability of clinical data means there is a wealth of opportunity to improve disease understanding and outcomes, through research and quality improvement methodologies. But with 80% of the richest insights trapped in unstructured text, AI techniques like NLP are a critical piece of functionality in any medical center. This presentation will look at the key capabilities for NLP and how interoperability and democratization of NLP functionality is vital in the following application areas: 1. Automated extraction of cancer insights from pathology reports for cancer registries, bio-specimen and research data warehouses 2. Exploration of large clinical data sets to support machine learning algorithm development, such as for opioid abuse prediction 3. Analysis of phenotypic characteristics in clinical data to support phenotype/genotype analysis 4. Identification of clinical trials candidates 5. • Advanced searching of scientific literature for rare disease associations with genetic variants