The invention provides an AI-based
system for real-time scheduling and automated rescheduling of diagnostic testing appointments across a network of clinics. The
system dynamically assigns appointments based on clinic availability,
patient location, test urgency, and insurance compatibility. It incorporates a self-learning AI model trained on historical appointment and wait
time data to optimize scheduling predictions and improve clinic
throughput. A real-time monitoring module continuously tracks clinic conditions and triggers automated notifications when projected delays exceed a predefined threshold. Patients are offered options to remain at the original clinic, reschedule to a nearby clinic with shorter wait times, or cancel the appointment. The
system includes a dual-view clinic interface (map and
list), handles both integrated and non-integrated clinics, and ensures HIPAA-compliant handling of personal and
health data. Integration with external clinic scheduling systems allows seamless data transfer and real-
time synchronization. This invention significantly improves patient experience and clinic efficiency by reducing wait times and minimizing manual rescheduling efforts.