Orthopedic and musculoskeletal practices increasingly find that money slips away at the front end of the revenue cycle, where incomplete documentation and manual coding can trigger preventable denials. And as payers lean more heavily on artificial intelligence in their own claims processing, the technology gap between providers and carriers may be widening — raising the cost of leaving coding workflows unchanged.
During a featured session at Spine, Orthopedic and Pain Management-Driven ASC + The Future of Spine Conference, Mark Herzog, principal solutions specialist at Veradigm, made the case for applying task-specific AI to the coding process to capture more revenue with less rework.
Note: Quotes have been edited for length and clarity.
1. Coding workflows haven’t changed much in 30 years
For most organizations, the coding process has stayed largely static for decades: providers document, a coder reviews and abstracts the note, and the claim goes out, often with little feedback returning to the provider. Meanwhile, the pressures around that workflow have intensified. Certified coders are increasingly expensive, hard to recruit and aging out of the field, with few new entrants replacing them.
2. Payers are already using sophisticated AI
Carriers have moved aggressively to deploy AI in claims adjudication, including automatic down-coding and front-end screening designed to deny claims before they fully enter the process, according to Mr. Herzog. He noted that roughly 15% of inbound claims are denied, and about half of those are never resubmitted, meaning carriers keep that money outright. The remainder cost provider organizations significant rework to recover.
3. AI works alongside coders — not instead of them
Despite market hype that AI will replace coding staff, Mr. Herzog argued the opposite. The most successful deployments position AI beside human coders, letting the engine consume the clinical note, abstract the codes and flag exceptions, while coders focus on complex cases, contradictions and provider re-education rather than routine claims.
The critical requirement is specialization. A general-purpose model fed clinical notes will underperform; organizations need AI built specifically for revenue cycle and coding, with MSK-specific regulations and carrier requirements embedded. After coding, a separate rules engine can apply payer-specific formatting before the claim is billed.
4. The financial case shows up quickly in the data
Mr. Herzog shared outcomes from two clients. One — a 60-provider group across six locations in five states, headquartered on the West Coast — found that 32% of cases had incorrect coding, 17% had recoverable increases in relative value units and 10% had documentation deficiencies. A second organization, a 150-provider group across 13 East Coast locations facing acute staffing shortages, saw a 38% reduction in charges returned to providers and a 58% improvement in charge lag.
Mr. Herzog stressed measuring against baseline benchmarks before implementation, and framed success around both operational efficiency and financial performance. He singled out timely-filing denials as fully preventable. “Timely filing should never, never be a reason not to get paid for a claim,” he said.
What this means for orthopedic leaders
Successful AI adoption hinges less on the technology than on the surrounding people and process, Mr. Herzog argued. Organizations must define their goals, secure leadership buy-in, prepare providers to accept documentation feedback and communicate the rollout openly. AI coding is now accessible to organizations of nearly any size, unlike the costly technology transitions of the past. With payers compounding their own AI advantage, the practices that move now stand to get paid more quickly and more fully, while those that wait keep paying twice for the same claim.
Learn More: Request a Demo | Veradigm
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