Real-Time Customer Support Chatbots for Enhancing Service Efficiency Through Advanced Language Models

This research investigates the creation and deployment of real-time customer support chatbots in the healthcare sector using advanced natural language processing models, including GPT-3, DialoGPT, and BERT. The main goal is to improve service effectiveness by delivering on-time and accurate support to patients, caregivers, and healthcare professionals. The research utilizes a specialized healthcare dataset consisting of 350 instances, including a variety of patient queries, medical terminology, and healing protocol framing. The dataset is divided into training and testing instances, with 70% of the data for training the NLP models and 30% for precision and recall evaluation. The models are finely tuned during the training process through the Hugging Face platform and TensorFlow framework to ensure efficient, compatible NLP model update and training. Performance is measured and observed on a range of metrics, such as precision, recall, F1 score, AVC-ROC, fluency, contextuality, and response duration. Results show that GPT-3 has excellent performance across most patterns with incredible accuracy, fluency, and contextuality as well as immediate reply intervals. DialoGPT and BERT also perform admirably but with slight variations in performance metrics. The confusion matrices demonstrate the precise nature of healthcare processor and responder instances for all models. This research contributes to the research of advanced NLP in healthcare, demonstrating how sophisticated language models can radically alter help servicing and patient care distribution.