Josh Black

DMEPOS suppliers are losing time and revenue in the same place: documentation gaps that surface too late to fix cheaply. 

Industry benchmarks put DME denial rates at roughly 15 to 18 percent, nearly double the broader healthcare average, and a significant share of those denials trace back to preventable workflow failures rather than complex clinical judgment calls: missing documentation, stale eligibility checks, wrong modifiers, expired prior authorizations. Most practices still catch these problems after a claim is submitted, when the fix costs far more than prevention would have.

This session offers a practical framework for understanding where AI genuinely helps and where it doesn't: closing the gap between documentation and denials, cutting the hours staff spend on rework and resubmission, and moving compliance checks upstream to intake and point of care instead of after the fact. 

We'll look at what "AI-assisted compliance review" actually means in practice, why it depends on clinicians and front-office staff as much as billers, and what real-world outcome data shows when practices make this shift, including de-identified case examples from O&P/CRT practices that have adopted pre-submission AI review.

Attendees will leave with a clear framework for evaluating AI-driven efficiency and compliance solutions: what to ask before adopting one, how to build an internal business case using their own numbers and comparable outcome data, and how to avoid treating this as a billing-only fix when it touches the whole practice.