Volume 2 - Issue 1, January - February 2026

Hospitals worldwide constantly grapple with the same core issues when it comes to managing their appointment systems: tangled schedules, high rates of patient no shows, and the resulting inefficient use of valuable resources like staff, equipment, and time. When a patient misses their slot without prior notice, that time isn’t easily recouped. Clinicians are left waiting, operational costs rise, and other patients may see their treatments delayed. This creates a ripple effect of frustration for both healthcare providers and the people seeking care. Traditional scheduling relies heavily on fixed rules, simple averages, or past trends, assuming patient behaviour is predictable and static. In reality, patient attendance is influenced by a mix of personal, temporal, and contextual factors, and static systems can’t keep up with this complexity. This research presents a dynamic AI-driven appointment scheduling system that leverages advanced machine learning techniques to proactively forecast which patients are most likely to no-show. By analysing historical appointment dataincluding patient demographics, attendance history, department-specific patterns, and even time-of-day trends, the system can assign a probability score to each appointment’s likelihood of being missed. This predictive insight allows hospitals to implement selective overbooking strategies, filling likely gaps without overwhelming staff or overcrowding waiting rooms. The machine learning models continuously refine themselves as more data is gathered, enabling the scheduling process to adapt to changing patterns over time. Experimental results show that such an intelligent, data-driven approach not only improves resource utilisation and reduces idle time for clinicians but also shortens wait times and enhances the overall operational flow within the hospital. The broader implication is clear: artificial intelligence, when thoughtfully implemented, has the potential to revolutionise appointment management, offering a practical solution to longstanding inefficiencies in healthcare delivery.

Sarik Anver J, J. Jelina,"Intelligent Scheduling with AI to Prevent Overbooking in Modern Systems" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(1): Page(1482-1485) Jan-Feb 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.