TEC04: AI in DME: Trends, Impact, and What’s Next

Denials remain one of the most persistent threats to cash flow, efficiency and reimbursement performance for HME providers. While strong processes, payer knowledge and disciplined follow-up are still essential, AI is changing how organizations prioritize and recover denied revenue. This session will explore how AI can help providers move from reactive denial management to more proactive revenue recovery, including practical examples of AI-driven denial prioritization. Attendees will learn how smarter work allocation can reduce wasted effort, improve staff efficiency, accelerate collections and increase revenue capture while keeping human expertise and operational guardrails at the center of the process.

Artificial intelligence has moved through distinct waves of development, each unlocking exponentially greater capability than the last. For DME providers, understanding this trajectory isn’t just academic, it’s the difference between reactive adoption and strategic advantage.

This session translates AI’s rapid evolution into concrete business value for DME organizations. We’ll also cover where in the AI tech stack the real value is being created. Attendees will leave with a clear framework for undertaking the AI transformation and how these capabilities translate into practical solutions for DME workflows, from intake and eligibility to fulfillment and patient engagement.

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.