Natural Language Chatbot System for Patient-Physician Matching Using SQL Database Integration

Patients often face challenges identifying appropriate healthcare specialists due to limited access to reliable provider information. This project introduces an intelligent chatbot designed to assist patients in locating suitable physicians based on their specific healthcare needs. The proposed system connects patients to a structured physician database hosted on the hospital server, containing details such as specialties, subspecialties, expertise areas, and availability. Leveraging natural language processing (NLP) algorithms, the chatbot interprets patient queries, such as “Which cardiologist treats heart failure?” or “Where is an orthopedic surgeon specializing in sports injuries located?”, and translates them into optimized SQL queries.

The system architecture comprises three main components: (1) an interactive chatbot that facilitates patient engagement, (2) a Python-based backend engine for NLP intent detection, entity recognition, and query formulation, and (3) a secure communication module that enables HIPAA-compliant access to the hospital’s SQL Server database. The chatbot employs schema-aware query construction to accurately align patient-described entities with corresponding database fields.

Compared with traditional provider directory searches, this system offers a more intuitive and intelligent interface for non-technical users. It personalizes provider recommendations, supports real-time access to the most current provider data, and enhances patient decision-making through accessible and accurate information delivery. This intelligent chatbot model demonstrates how natural language interfaces can improve patient navigation, provider matching, and information retrieval in healthcare delivery systems.