Research described Natural Language Processing (NLP) tools that outperform ICD-10 coding in capturing clinically relevant information, highlighting limitations of structured diagnosis coding for nuanced clinical interpretation. The report frames NLP as improving how patient record content is extracted into usable data. In healthcare informatics, ICD-10 coding can miss context and granularity because it relies on standardized billing or documentation categories rather than the full clinical narrative. The findings suggest NLP can better map clinical text to clinically meaningful features. For biotech teams using real-world evidence, NLP-based extraction can improve cohort definition and outcome tracking, potentially affecting downstream analyses for trials and post-marketing studies.