Should I use AI in my Orthopedic Practice RCM?

An orthopedic group with 15 providers submits roughly 39 prior authorization requests per physician per week according to AMA's 2024 survey of 1,000 practicing physicians. AMA also found that physicians and staff spend approximately 13 hours per week completing those requests. Forty percent of practices have hired staff who work on nothing else.

The revenue cycle is broken. Everyone agrees. The question is what kind of broken it is. In the era of constant AI innovation and tool adoption, that distinction matters more than ever.

There are two fundamentally different categories of work inside orthopedic revenue cycle management.

The first category is deterministic. A prior authorization either exists or it does not. A provider's credentialing with a payer is either active or expired. A CPT code either matches the documentation or it does not. An eligibility check returns a binary result. These are not judgment calls. They are lookups, comparisons, and status checks against known rules and known data.

The second category is probabilistic. Predicting which claims will be denied. Determining optimal appeal language for a specific payer. Forecasting revenue based on payer mix trends. Recommending coding strategies that balance reimbursement against audit risk. These require inference from incomplete data, the kind of work where an experienced RCM director's intuition has genuine value.

The AI conversation in healthcare has conflated these two categories almost completely.

The 2025 CAQH Index, released in February 2026, found that US healthcare avoided an estimated $258 billion in administrative costs through electronic transactions and automation in 2024 — a 17 percent increase over the prior year. But a remaining $21 billion savings opportunity still exists through full automation of manual and partially manual transactions. These are not ambiguous, judgment-intensive workflows. They are eligibility verifications, claim status inquiries, and prior authorization submissions that are still being completed by a person calling a portal or navigating a phone tree.

In orthopedics specifically, the complexity is real but not mysterious. There are hundreds of anatomically specific CPT codes. Modifier usage for bilateral and multi-level procedures has precise rules. The 90-day global period for fracture care has explicit documentation requirements. CMS released the FY2026 ICD-10-CM update effective October 1, 2025, introducing 487 new codes, 38 revisions, and 28 deletions across all chapters, including expanded musculoskeletal codes affecting orthopedic practices. And payers are now running their own AI review systems that evaluate authorization requests against clinical criteria databases before a human reviewer even touches them.

All of this is complex. None of it is ambiguous.

MIT's NANDA initiative published a report in July 2025 titled "The GenAI Divide: State of AI in Business 2025" that found 95% of enterprise generative AI pilots delivered no measurable P&L impact. The core finding was not that the models were bad. It was that implementations failed because they did not adapt to context, did not retain feedback, and did not integrate into actual workflows.

Kimble Jenkins, CEO of OrthoSouth in Memphis, referenced this study directly when asked what would define orthopedics in 2026. His assessment was blunt: 2025 was largely a year of hype and aggressive sales pitches, and he believes 2026 will be the year a handful of practices begin deploying AI in ways that produce real, measurable ROI.

Michael Doyle, Vice President of Orthopedic Services at Heartland Orthopedic Specialists, described the situation from the practice operations side: The historic slide of reimbursement paired with continuous increases in expenses is at a crossroads, and increasing administrative processes from prior authorization to medical necessity requirements to down coding and denials will continue to strain a system already facing financial shortfalls.

The pattern these leaders are describing is not a technology problem. It is a targeting problem. Most AI investment in healthcare is aimed at the probabilistic layer — predicting denials, generating appeal letters, forecasting revenue — while the deterministic layer, where the actual dollars leak, remains stubbornly manual.

CMS finalized its Prior Authorization Reform Rule (CMS-0057-F) in January 2024, with key provisions taking effect January 1, 2026.,The rule cut payer decision windows from 14 days to 7 calendar days and mandated electronic prior authorization for all Medicare Advantage and commercial payers. The WISeR Model, effective January 2026, introduced AI-driven prior authorization and early claims review across 17 service categories. Payers are automating their side of the equation. Practices that do not automate theirs will be structurally disadvantaged.

Michael Stauff, MD, an orthopedic spine surgeon and department chair at UMass Chan Medical School, put the clinical reality plainly: preauthorization requirements for elective procedures are delaying patient care, expanding back-office administrative burden, and placing peer-to-peer review demands on surgeons — all driven by what he described as haphazardly created payer criteria.

The solution is not a language model that generates authorization requests. It is software that knows which patients on tomorrow's schedule require authorization, which authorizations are expiring, which payer requires which documentation, and which credentialing relationships are active. The rule sets are enormous and they change constantly, but the work is deterministic. AI's role is building against that complexity: normalizing payer rules, propagating regulatory changes before they generate denials, and translating between systems that were never designed to talk to each other.

Notable's Dayne Hoffman framed this well in early 2025: AI's first real proving ground will not be in the exam room, it will be in the revenue cycle. The goal is not to replace people, but to unburden them. Experian Health's 2026 data bears this out — 63 percent of providers have introduced AI in some capacity, but only 15 percent have fully integrated it into standard RCM operations. The gap between adoption and integration is the entire story.

If you run an orthopedic group, the question is not whether to adopt AI. It is whether the AI you adopt is aimed at the right layer.

Probabilistic tools — denial prediction, appeal optimization, revenue forecasting — have value. But they are second-order improvements on a first-order problem. The first-order problem is that deterministic work is still being done manually by people who are expensive, hard to recruit, and burning out.

A credentialing lapse is not a prediction problem. It is a monitoring problem. A missed prior authorization is not a language generation problem. It is a scheduling and status-check problem. Month-end reporting that takes three senior staff members two days to assemble is not an analytics problem. It is a data pipeline problem.

The practices that will outperform over the next three years are the ones that automate the deterministic layer first, completely, with systems that are maintained against the constant churn of payer rules and regulatory changes. Then they deploy their people — and their probabilistic tools — against the ambiguous work that requires human judgment.

The complexity is real. The ambiguity, mostly, is not.