Natural Language Processing (NLP) is transforming healthcare decision-making by extracting valuable insights from Electronic Health Records (EHR). This paper explores the integration of NLP with EHR systems, focusing on its potential to enhance clinical workflows, patient outcomes, and the accuracy of healthcare decision-making. Using advanced NLP techniques, such as BERT and spaCy, the study analyzes both structured and unstructured EHR data to uncover patterns in diagnosis, treatment recommendations, and patient outcomes. The study compares NLP-based analysis with traditional data analysis methods, demonstrating its effectiveness in improving clinical decision support systems. Despite the promising potential,challenges such as data quality, model interpretability, and seamless integration with existing healthcare systems are highlighted. The paperconcludes by emphasizing the need for continued advancements in NLP models and real-time data processing, suggesting future research directions to optimize the implementation of NLP in healthcare settings