EVALUATING THE EFFECTIVENESS OF NLP-POWERED CHATBOTS IN PATIENT SUPPORT AND EDUCATION

This systematic review aims to evaluate the effectiveness of Natural Language Processing (NLP)-powered chatbots in patient support and education within healthcare settings. As AI-driven technologies, particularly chatbots, become more prevalent in healthcare, this study seeks to assess their potential in improving patient engagement, educational outcomes, chronic disease management, and mental health support. The review will address key research questions, including the features that enhance the effectiveness of NLP-powered chatbots, how they compare to traditional patient support methods, and the impact of demographic factors on their success. The study will also explore patient perceptions of chatbot reliability, as well as ethical, privacy, and accessibility concerns associated with their use. Research Questions: What are the key features of NLP-powered chatbots that enhance patient engagement and education? How do NLP chatbots compare to traditional methods in terms of patient comprehension of medical conditions? What is the role of demographic factors (age, literacy, tech-savviness) in the effectiveness of NLP chatbots for patient support? What measurable outcomes are associated with the use of NLP chatbots for chronic disease and mental health support? How do patients perceive the reliability and accuracy of NLP chatbot information? What barriers hinder the adoption and effectiveness of NLP-powered chatbots in healthcare? What ethical, privacy, and trust considerations exist for using NLP chatbots in healthcare? The study will be conducted using a systematic review methodology, with comprehensive searches in databases like Web of Science (WoS) and PubMed. Inclusion and exclusion criteria will focus on studies published between 2018 and 2025, involving NLP-powered chatbots in healthcare settings. The expected outcomes include identifying effective chatbot features, understanding patient engagement impacts, and addressing barriers to adoption. Insights from this review will guide future research and inform the development and integration of NLP chatbots into healthcare systems, ultimately improving accessibility, engagement, and patient outcomes.