The DME leader’s guide to success in the age of AI

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.

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.

HME providers are under constant pressure from rising labor costs, reimbursement compression, denials, documentation burden, payer complexity, and operational inefficiency. Yet many organizations still evaluate profitability decisions using surface-level metrics like allowed amounts, order volume, or top-line revenue.

This interactive workshop will show participants how to use EHR and operational data to understand their true cost-to-serve. Attendees will learn how intake, documentation, prior authorization, delivery, billing, collections, denials, rework, and overhead all impact profit.

Using a mock HME case study, attendees will work through a practical worksheet to calculate net realized revenue, direct cost, hidden labor, total cost-to-serve, and margin by payer, product line, and referral source. The session will also explore how these insights can inform payer negotiations, referral strategy, competitive bidding decisions, health-system contracts, managed care relationships, and operational improvement priorities.

Attendees will leave with a usable framework for identifying where margin is being protected, where it is being lost, and what data they need from their systems to make smarter business decisions.