A wealth of information is collected in the process of health care delivery, and critical information such as disease diagnosis, labs, and procedures, which exist in EHR records as structured data, are routinely used to power research, quality improvement, and AI/ML model development. However, 80% of the data in EHR records is in unstructured clinical documents, which is an untapped rich source of additional data. Annotating this rich source of information using clinical natural language processing (cNLP) provides important additional data points for these activities. Clinical NLP uses specialized content and techniques to extract valuable data from unstructured narrative clinical documents such as clinical notes, history and physicals, discharge summaries, pathology reports, and more.