Natural Language Processing in Digital Health: Transforming Clinical Narratives into Actionable Intelligence
Authors:-Vishal K
Abstract-:Natural Language Processing (NLP), a subfield of artificial intelligence, is revolutionizing digital health by converting unstructured clinical narratives into structured, actionable intelligence. With the exponential growth of electronic health records (EHRs), clinicians and researchers are confronted with vast amounts of textual data that often remain underutilized. NLP addresses this challenge by enabling automated extraction, interpretation, and analysis of clinical texts such as physician notes, discharge summaries, and pathology reports. This review explores how NLP is being leveraged across healthcare domains, from improving patient outcomes and streamlining administrative processes to supporting research and population health surveillance. It discusses key applications such as clinical decision support, disease surveillance, sentiment analysis, and adverse drug event detection. The article further examines current challenges including data privacy, accuracy of language models, and domain-specific language barriers. As the healthcare ecosystem increasingly integrates AI technologies, NLP stands out for its ability to decode human language and deliver meaningful insights from data. The future of digital health will depend heavily on the maturation of NLP tools, which can democratize access to information and personalize healthcare delivery. This review serves as a comprehensive guide to understanding the role, advancements, and implications of NLP in transforming modern clinical practice.
