Semantic search-enhanced healthcare chatbot for hospital information management system using vector database and transformer models

Abstract

Healthcare chatbots are increasingly used to assist hospital staff, yet most existing systems rely on rule-based or generic machine learning (ML) approaches that lack the ability to comprehend natural language queries, while proprietary deep learning systems often incur high licensing costs. This work addresses this gap by proposing a cost-effective and scalable semantic vector retrieval solution for user intent recognition in a hospital information management system (HIMS) helpdesk chatbot. The MPNet based transformer model is employed to convert user inquiries and predefined intents into feature vectors, enabling highly accurate natural language understanding through cosine similarity retrieval within a dedicated vector database. The proposed vector search method was validated via an ablation study, achieving an accuracy of 0.70 for intent recognition, which demonstrates a significant performance gain of 28.0 percentage points over a traditional keyword-based search baseline. Usability testing across developer and doctor groups yielded an average score of 7.78 on a 10-point Likert scale. This study concludes that integrating semantic vector retrieval with a vector database is highly effective for recognizing specialized clinical intents, offering a more accurate solution that significantly reduces the manual helpdesk workload and enhances 24-hour assistance in healthcare.