Research described in the excerpt reports that natural language processing (NLP) tools can outperform ICD-10 coding systems at capturing clinically relevant information. The work suggests NLP can extract more signal from clinical text than reliance on standardized diagnostic codes alone. ICD-10, a long-used taxonomy for diagnoses in healthcare billing and reporting, may miss nuance when symptoms, severity, and clinical context are represented informally in notes. NLP systems, by contrast, can translate narrative documentation into structured variables that better reflect clinical meaning. For biotech and clinical operations, improved data capture can strengthen downstream use cases including trial feasibility, endpoint extraction, and real-world evidence generation. When data quality improves, models that depend on retrospective cohorts can also become more reliable. The excerpt positions the finding as a step toward “more nuanced insights” from patient records, with implications for diagnostic precision and interpretability across clinical databases.