Natural Language Processing for Automated Extraction and Structuring of Unstructured Clinical Notes

Abstract

The exponential growth of unstructured textual data within Electronic Health Records (EHRs) poses significant challenges to modern healthcare analytics, clinical decision support, and patient outcome optimization. Narrative clinical notes including discharge summaries, physician progress notes, radiology reports, and laboratory documentation contain rich contextual information essential for diagnosis, treatment planning, and prognostic assessment. Manual abstraction of these notes is labor-intensive, error-prone, and often inconsistent, creating critical bottlenecks in both research and operational healthcare environments. Natural Language Processing (NLP), a subfield of artificial intelligence concerned with the computational understanding of human language, offers promising solutions for the automated extraction and structuring of such unstructured data. This paper presents a comprehensive examination of NLP methodologies applied to clinical text, ranging from traditional rule-based and statistical approaches to contemporary deep learning and transformer-based architectures, including BERT, BioBERT, and ClinicalBERT. The integration of these techniques with clinical workflows, augmented by Explainable AI (XAI) methods to ensure transparency and interpretability, can substantially enhance diagnostic accuracy, facilitate treatment optimization, and improve operational efficiency. Empirical studies and illustrative case analyses highlight current applications and limitations while addressing challenges related to privacy, ethics, interoperability, and generalizability across institutions. This study consolidates theoretical frameworks, methodological rigor, and practical insights to advance interdisciplinary understanding and adoption of NLP in healthcare (Fatunmbi, 2022; Fatunmbi, Piastri, & Adrah, 2022).