Worldwide, healthcare systems face increasing pressure to deliver timely, accessible and efficient services. Traditional appointment scheduling methods, based on phone calls and manual entry, have an average no-show rate of 23% with regular double bookings, scheduling conflicts and administrative overload. This paper presents Apointix, an AI-enabled web-based doctor appointment system, designed to systematically address these crucial gaps in traditional healthcare delivery.

Apointix is built on a MERN stack (React.js, Node.js, Express.js, MongoDB) that provides real-time doctor availability, role based access control, automated email and SMS notifications, smart conflict resolution, and priority scheduling for critical patients. It also has additional modules for insurance verification, online lab test booking and secure payment and refund system. The system also includes machine learning models for prediction of disease in three validated clinical datasets, namely, PIMA Indian Diabetes Dataset (85.2% accuracy), Cleveland Heart Disease Dataset (82.7%), and Parkinson’s UCI Voice Dataset (88.4%) to show AI-assisted diagnostic capabilities. Automated detection of scheduling conflicts eliminated all conflicts, and user acceptance testing showed a reduction in average appointment booking time from 12–15 minutes to under 2 minutes. The modular, scalable architecture of Apointix provides a solid foundation for future intelligent healthcare scheduling systems. Limitations of the current implementation include the absence of a native mobile application and live insurance API connectivity.