As healthcare evolves towards value-based care many healthcare organizations are realizing the value of unstructured data - because many important contributing factors on why patients are improving or worsening are often hidden away in text. Better patient outcomes are reliant on combining unstructured and structured data to give a true 360-degree view of the patient. Significant insights can be gained from clinician and nurse notes, radiology and pathology reports, and laboratory reports. Enterprise Natural Language Processing (NLP) technologies are needed to support the many varied applications that require input from these clinical documents and reports - freeing the insights and features trapped in EHRs to improve patient outcomes and increase clinician efficiency. In this session you will learn how NLP enables you to: Support extraction of clinical phenotypes from the EHR to improve the diagnostic yield of genomic tests; Enhance clinical research and Real-World Evidence projects with faster cohort selection and large-scale extraction of outcomes; Improve identification of clinical trials candidates from real time EHR data; Power predictive clinical models with Social Determinants of Health (SDOH) features; Augment patient safety and quality initiatives such as screening for early signs of cancer; Improve the efficiency of quality reporting and disease registry processes, reducing manual chart review