A Review of Natural Language Processing (NLP) for Clinical Documentation

Electronic Health Records (EHRs) contain a wealth of critical patient information, but the vast majority of this data is locked within unstructured clinical notes, discharge summaries, and pathology reports. This systematic review (2020-2025) charts the rapid advancements in Natural Language Processing (NLP) designed to unlock this data. We analyze the paradigm shift from traditional regex and dictionary-based methods to sophisticated deep learning models, particularly large language models (LLMs) and transformer architectures like BERT, fine-tuned for the medical domain. Key applications reviewed include: (1) information extraction for identifying patient symptoms, medications, and diagnoses; (2) automated ICD-10 coding to reduce administrative burden; (3) cohort identification for clinical trials and epidemiological research; and (4) patient summarization to support clinical handoffs. Despite significant progress, challenges in handling medical abbreviations, negations, and ensuring patient privacy (de-identification) remain. This review provides a comprehensive overview for researchers and healthcare administrators on the current capabilities and future trajectory of NLP in medicine.