Rocky Lulu

AI OPD Load Balancer

Hospitals, especially OPD departments, often face overcrowding, long waiting times, and inefficient patient flow management. Patients frequently arrive during peak hours without visibility into expected crowd levels, leading to frustration, delayed consultations, and poor healthcare experiences. In many hospitals, appointment scheduling and queue management systems are either manual or provide no prediction of future patient load. This results in uneven distribution of patients throughout the day, excessive waiting periods, and underutilization of available slots. Our solution addresses this Process We started by researching common challenges faced by patients in hospital OPDs and identified long waiting times as a major issue. We analyzed existing appointment systems and found that most platforms only allow booking but do not provide crowd predictions or waiting-time insights. We designed a workflow that combines patient load forecasting, queue management, and appointment scheduling into a single platform. Initially, we explored a basic appointment-booking system but realized it would not solve overcrowding. We then shifted our focus toward predictive queue optimization. Using simulated hospital data, we developed patient-load prediction models and designed an intuitive dashboard to visualize crowd levels across hospitals and departments. We iteratively refined the UI to resemble r Results The AI OPD Load Balancer successfully demonstrates how predictive analytics can improve patient flow and reduce waiting times in hospital OPDs. Key outcomes include: Predicted patient load across multiple hospitals and departments. Recommended optimal visit times based on expected crowd levels. Enabled appointment booking with automated token generation. Provided live queue tracking and estimated waiting times. Offered hospital comparison based on crowd intensity and availability. Improved decision-making for patients before visiting hospitals. Based on simulated testing, the system can pot Reflection If given more time and access to real hospital data, I would focus on integrating the platform directly with Hospital Management Systems (HMS) to obtain real-time patient flow information instead of relying on simulated datasets. I would also conduct usability testing with patients, reception staff, and hospital administrators to better understand operational challenges and improve the user experience. Additionally, I would implement advanced machine learning models for more accurate crowd prediction, add multilingual support for wider accessibility, and develop a mobile application with notif