Author(s) :

Aditya Narayan.

Volume/Issue :

Abstract :

Healthcare data is produced at an unprecedented scale worldwide, consisting of both structured and unstructured components. Structured data such as lab values, vital signs, and diagnostic codes are traditionally easier to analyze, but they represent only a fraction of the total healthcare information. A vast majority of clinical data exists in unstructured formats, including free-text clinical notes, imaging reports, pathology narratives, and patient communications, which contain rich contextual and nuanced information. Natural Language Processing (NLP) has emerged as a critical technology for unlocking this underutilized resource by converting unstructured text into structured, computable data. This paper reviews the state-of-the-art NLP methodologies applied in healthcare, highlighting their role in transforming unstructured data into actionable knowledge. It explores key NLP techniques such as named entity recognition, relation extraction, sentiment analysis, and deep learning frameworks. The paper discusses diverse applications ranging from clinical documentation improvement and decision support to research and population health management. Challenges related to linguistic variability, domain adaptation, data privacy, and system integration are analyzed. Future prospects include real-time NLP, multilingual capabilities, and explainability, which promise to accelerate the integration of NLP-driven insights into clinical practice and research, ultimately enhancing patient care quality and operational efficiency.

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