Natural language processing (NLP) in healthcare unlocks insights from unstructured clinical data, which often contains critical findings hidden in physician notes and reports. Effective NLP requires three key factors: eliminating bias by training on diverse populations, understanding clinical context to better interpret findings, and distinguishing normal from abnormal cases through exposure to both positive and negative reports. Unlike rigid regex methods, AI-powered NLP adapts to variability in language and metadata, enabling precise patient identification, streamlined care pathways, and improved nurse navigator efficiency. This enhances care quality, reduces wasted resources, and strengthens patient outcomes.

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