MEDIQUICK – Optimized Emergency Bed allocation using AI and IoT

Abstract

MediQuick is an AI and IoT-powered system that simplifies emergency hospital bed booking. It enables patients to input symptoms through a web interface, which are analyzed using a Logistic Regression model to determine if the condition is an emergency. If confirmed, a Random Forest classifier further identifies the emergency category. The system then uses the patient’s location to search for nearby hospitals with both available beds and doctors specialized in the detected condition. Once a suitable match is found, a temporary bed reservation is made to hold the spot. If the patient fails to arrive within the given time frame, the bed is automatically released for others in need. An IoT module, powered by a Force Sensing Resistor (FSR) connected to a NodeMCU, detects real-world patient presence on the bed to confirm physical admission. This allows seamless, automatic check-in without manual hospital intervention. Flask is used to build the backend server, while MySQL handles all hospital, bed, and patient data. Python’s machine learning ecosystem powers the emergency prediction models. MediQuick enhances the responsiveness of emergency healthcare systems through intelligent automation and real-time decision-making. The system supports efficient triaging and optimizes resource usage in critical situations. It bridges the gap between patients and hospitals using smart technology. MediQuick ultimately delivers a scalable and life-saving solution for modern healthcare challenges.Traditional sentiment analysis tools often fall short by offering only basic polarity classification and failing to provide detailed understanding of specific customer concerns.