An AI-NLP based Interactive Chatbot Model for Patient Prescreening at Doctor’s Consultancy

Diabetes is a prevalent issue in Bangladesh, presenting significant challenges for healthcare systems. This project aims to develop an integrated health prescreening solution that combines an interactive chatbot with sensor modules for vital health data logging. Effective diabetes treatment requires comprehensive information from patients, including lifestyle, dietary habits, medications, and comorbidities, which typically consumes a lot of time during medical consultations. The proposed system streamlines this process by using dedicated sensors to collect essential health data, such as temperature, heart rate, and oxygen levels. Patients interact with a conversational chatbot to provide additional information, including symptoms and medical history. This data is then displayed on a screen at the end of the conversation for easy reference by healthcare professionals. The project progresses through four main stages: requirement analysis, system design, chatbot development, and sensor integration. While the sensor output may show slight fluctuations compared to medical-grade equipment, the chatbot achieves high accuracy ($\gt \mathbf{9 0 \%}$) in conversation and information extraction. By integrating vital sign monitoring with an interactive chatbot, this project aims to reduce waiting times, enhance efficiency of medical checkups, and improve doctor-patient communication in a modern healthcare setting.