Enhancing E-Health with Natural Language Processing: The Role of Sentiment Analysis in Modern Healthcare

Abstract

The advent of e-health systems has significantly addressed the challenges of inefficiencies, high operational costs, and limited accessibility in traditional healthcare systems. This paper explores the critical role of Natural Language Processing (NLP) in e-health systems, particularly the transformative potential of sentiment analysis in improving patient care and system efficiency. The study employs NLP techniques to analyze and interpret large volumes of healthcare data, utilizing sentiment analysis to extract emotional information from patient experiences. This information helps identify patient sentiments towards treatments and services, thereby enhancing patient satisfaction and care quality. Additionally, sentiment analysis plays a crucial role in mental health monitoring by identifying potential issues based on patients' emotional expressions. The results indicate that NLP-driven sentiment analysis can significantly improve patient satisfaction, enhance mental health monitoring, and support more effective public health strategies. By leveraging sentiment analysis, healthcare providers can better understand and respond to community needs, ultimately leading to better healthcare outcomes. This research provides important insights for the future development of e-health systems, particularly in handling unstructured data, improving data analysis accuracy, and enhancing decision support.