Strategic Rationale

Post-COVID, Inova recognized they couldn't scale staffing sufficiently to meet growing demand from patients calling the system to make or change appointments. They had ~300 largely local remote contact center agents taking 2.4 million calls annually—over 3 million by the end of 2025—but needed greater efficiency. Most inquiries centered on appointment management, but staffing constraints and lengthy hold times created service bottlenecks, prompting many patients to abandon calls instead of confirming or adjusting their appointments. This resulted in higher no-show rates and unused appointment slots. Inova wanted to use agentic AI (specifically machine learning models that don't hallucinate, following pre-programmed logic) to handle lower-complexity, repetitive work and free human agents for higher-complexity patient needs.