GoHealthcare Knowledge Center | Orthopedic Surgery

AI Governance in Orthopedic Practices

Governed applications of artificial intelligence in documentation, coding, authorization, denial prediction, scheduling, outcomes, and payer utilization review.

Developed by Pinky Maniri, MSc, CRCR, CSAPM, CSPPM, CSBI, CSPR, CSAF, Certified in Healthcare A.I. Governance | Founder and Chief Executive Officer, GoHealthcare Practice Solutions

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Important Notice This page is an educational and operational reference. It is not a surgical technique manual, a substitute for physician clinical judgment, coding advice for a specific claim, or legal advice. Coverage, authorization, coding, and payment requirements vary by payer, product line, state, contract, and date of service. Verify all requirements before reliance.

Artificial intelligence can reduce administrative burden and improve completeness, but it cannot replace coding authority, payer policy verification, or accountable human judgment. This page defines governed use in orthopedic documentation, authorization, coding, scheduling, outcomes, and payer review.

Specialty Orthopedic Surgery

Focus Operations, payer policy, documentation, coding, compliance, and performance

Audience Physicians, APPs, administrators, PA/UM, RCM, ASCs, hospitals, and executives

Verification Standard Confirm current payer, CMS, coding, contractual, and state requirements

Page Contents

AI Applications GoHealthcare Leadership Perspective Frequently Asked Questions Authoritative References

Orthopedic Specialty Center

Orthopedic Surgery Specialty Guide Executive entry point for orthopedic service-line operations and the complete Orthopedic Surgery Specialty Hub. Orthopedic Practice Operations Operating model, scheduling, site-of-service, staffing, and pre-surgical huddle controls. Orthopedic Prior Authorization Decision pathways, payer criteria, authorization workflow, peer-to-peer, and appeals. Orthopedic Revenue Cycle Management Revenue leakage controls, financial clearance, payment changes, and RCM performance. Orthopedic Documentation Requirements Clinical documentation checklists for authorization, coding, payment, and audit defense. Orthopedic Coding and Billing Fundamentals CPT families, diagnosis coding, modifiers, place of service, and coding risk. Orthopedic Compliance and Audit Readiness Federal and payer compliance, audit risk, governance, and corrective controls. Orthopedic KPIs and Performance Management Definitions, owners, thresholds, and interventions for orthopedic performance metrics. AI Governance in Orthopedic Practices AI use cases, governance requirements, payer-side AI, and human accountability. Orthopedic Best Practices and Common Mistakes Best practices, common mistakes, operational pearls, pitfalls, and takeaways. Orthopedic Procedure Library Procedure categories and links across the GoHealthcare MSK Specialty Procedure Library. Orthopedic Surgery FAQs Twenty-five substantive orthopedic operations, payer, documentation, coding, and billing FAQs.

AI Applications

Artificial intelligence has moved from pilot to production on both sides of the orthopedic authorization transaction. Payers use it to triage and adjudicate. Practices use it to draft, abstract, code, and predict. The regulatory environment that governs both uses changed materially during 2025 and 2026, and it changed primarily at the state level. This section covers where AI is genuinely useful in an orthopedic service line, what governance obligations attach, and what a practice can insist upon when a payer uses AI against it.

Where AI creates real operational value in orthopedics

Application	What it does	Governance requirement
Ambient clinical documentation	Generates a draft clinical note from the encounter, reducing documentation burden and improving the completeness of the structured elements that authorization criteria require	Physician attestation and review before signature; disclosure to the patient where state law requires it; retention and privacy analysis for the audio and transcript
Criteria matching and packet assembly	Compares chart content against a published payer or vendor criteria set and identifies missing elements before submission	The tool must not fabricate clinical content; outputs are a completeness check, not a source of clinical assertions; human review before submission
Coding assistance and audit	Suggests codes and modifiers from the operative report and flags documentation insufficiency	Coder review and sign-off; the practice remains responsible for the claim regardless of the tool's suggestion; accuracy monitoring with a documented sampling plan
Denial prediction and triage	Scores pending claims and authorizations for denial likelihood so that scarce staff attention is directed where it changes the outcome	Model performance monitoring; guard against systematically deprioritizing a patient population
Prior authorization status automation	Retrieves and reconciles authorization status across portals, reducing manual checking	Credential management and access control; audit logging
Scheduling and capacity optimization	Predicts case duration, cancellation likelihood, and block utilization	Transparency to clinical leadership; avoid embedding payer-mix or demographic variables that create access disparity
Outcome capture and patient engagement	Automates pre-operative and post-operative patient-reported outcome collection and follow-up	Consent and communication preferences; escalation pathway for clinically concerning responses

Denial Risk

Do not use a general-purpose language model as a source of coding or coverage truth. GoHealthcare's project experience across this Library is unambiguous on this point: fabricated codes, obsolete descriptors, and incorrect payer positions have repeatedly entered draft work product from AI-generated and secondary sources, including from peer-reviewed literature that itself cited a deleted code. AI is appropriate for drafting, structuring, summarizing, and checking completeness. It is not appropriate as the authority for a code, a descriptor, a coverage position, or a policy effective date. Those come from the primary source, every time.

Payer use of AI in utilization review

CMS has permitted Medicare Advantage plans to use artificial intelligence to assist prior authorization determinations while requiring that the tools account for the beneficiary's individual clinical circumstances and the treating physician's recommendations, and that they not rely on datasets that fail to account for the individual's specific medical circumstances. In parallel, CMS itself introduced technology-assisted review into Original Medicare through the WISeR Model, in which model participants perform medical necessity review assisted by technology including artificial intelligence and machine learning.

The more consequential development, however, is at the state level. Legislatures have enacted a rapidly growing body of law restricting how insurers may use artificial intelligence in coverage determinations. The common architecture across these statutes is consistent even where the details differ.

• A licensed clinician, not an algorithm, must make an adverse medical necessity determination. Several states now provide that artificial intelligence may not be the sole basis to deny, delay, or modify a service, and that a licensed physician or other qualified health professional must make the determination.
• Individualized review is required. Statutes commonly require that the human reviewer consider the requesting provider's recommendation, the enrollee's medical or clinical history, and the enrollee's individual clinical circumstances, rather than relying on group-level datasets.
• Disclosure and transparency obligations attach. Several states require insurers to disclose their use of artificial intelligence in utilization review policies and procedures, and in some cases to enrollees and providers directly.
• Certification, audit, and non-discrimination requirements are emerging. Certain statutes require annual certification to the state regulator that the tool does not rely on a group dataset, is applied fairly, and does not discriminate, and subject the tool to regulatory inspection.
• Initial review versus adverse determination is a recurring distinction. Some states expressly permit AI-assisted initial review while prohibiting AI-issued adverse determinations, which means the practical question in an appeal is not whether AI was used but at which step it was used.

Operational Action

Ask the question, on the record. When an adverse determination arrives, GoHealthcare recommends a standing appeal element that asks the payer to identify: the guideline name, number, and version applied; the specific criterion found unmet; the name and credentials of the individual who made the determination; and whether an automated or algorithmic tool contributed to the determination and at which step. In states with AI utilization review statutes, this question is not rhetorical; it establishes the record for a regulatory complaint if the answer is unsatisfactory. Verify the specific requirements and effective dates for each state in which you operate, because this body of law is changing on a legislative session cadence and effective dates are staggered across 2026 and 2027.

An orthopedic AI governance framework

GoHealthcare recommends that any orthopedic practice deploying AI in clinical, documentation, coding, or authorization workflows adopt a written governance framework covering the following domains. This framework is operational rather than legal, and should be reviewed by counsel and by the organization's privacy and security function before adoption.

• Inventory. A maintained register of every AI-enabled tool in use, its vendor, its function, the data it touches, and the workflow it sits in
• Purpose limitation. A written statement of what each tool may and may not be used for, including an explicit prohibition on using generative tools as a source of coding or coverage authority
• Human accountability. A named human reviewer accountable for every output that reaches a clinical record, an authorization submission, or a claim
• Attestation and disclosure. Physician attestation for AI-drafted clinical content; patient disclosure where state law requires it; disclosure of AI-assisted authorship where the organization publishes
• Accuracy monitoring. A documented sampling plan measuring the tool's accuracy against human review, with a defined threshold that triggers suspension
• Privacy, security, and data use. Business associate agreements; verification of whether vendor terms permit use of the organization's data for model training, and an affirmative position on that

question

• Bias and equity review. Assessment of whether any tool that prioritizes, triages, or predicts could systematically disadvantage a patient population
• Vendor diligence and contract terms. Model provenance, update and versioning practice, incident notification, indemnification, and audit rights
• Incident response. A defined pathway when a tool produces an erroneous output that reaches a record, a submission, or a claim, including overpayment analysis
• Training and competency. Role-specific training that covers both capability and limitation, refreshed when tools materially change
• Documentation of the governance itself. Minutes, decisions, and review dates retained, so that the organization can evidence its governance rather than assert it

Leadership Perspective

The governance question that separates mature orthopedic organizations from the rest is not which tools do we use but who is accountable for the output. A tool that drafts a note does not sign it. A tool that suggests a code does not submit it. A tool that predicts a denial does not appeal it. In every case a named human carries the professional and legal responsibility, and the organization's governance should make that person's identity and obligation explicit rather than diffuse.

GoHealthcare Leadership Perspective

Artificial intelligence and the accountability question

Artificial intelligence will reduce documentation burden, accelerate coding, and improve triage in orthopedic operations, and organizations that decline to adopt it will carry a cost disadvantage. That is not the executive question. The executive question is governance: who is accountable for each output, what the tool may not be used for, how accuracy is monitored, whether vendor terms permit the organization's data to train a model, and how the organization would evidence its governance if asked. Simultaneously, payers are deploying the same class of technology against the practice, and a growing body of state law now constrains how they may do so. A practice that understands its own governance obligations is also, not coincidentally, the practice best positioned to challenge an algorithmic denial.

Frequently Asked Questions

The answers below are operational guidance and are not coverage determinations. Verify payer-specific positions against the current applicable policy.

Can a payer deny our request using artificial intelligence?

The answer is increasingly jurisdiction-specific. A growing number of states now require that an adverse medical necessity determination be made by a licensed physician or other qualified health professional and prohibit artificial intelligence from serving as the sole basis to deny, delay, or modify a service. Several also impose disclosure, individualized-review, certification, and audit requirements. Verify the statute and effective date for each state in which you operate, and ask on the record whether an automated tool contributed to the determination and at which step.

Can we use AI to write our notes and pick our codes?

AI is appropriate for drafting, structuring, summarizing, and checking completeness, under a governance framework with a named accountable human reviewer. It is not appropriate as the authority for a code, a descriptor, a coverage position, or a policy effective date. Those come from the primary source. GoHealthcare's experience across this Library is that fabricated codes and obsolete descriptors enter work product through exactly this shortcut.

Authoritative References

Coverage policies, code sets, model parameters, and utilization management criteria change. Verify currency at the time of use.

• CMS Interoperability and Prior Authorization Final Rule: https://www.cms.gov/newsroom/fact-sheets/cms-interoperability-prior-authorization-final-rule-cms-0057-f
• CMS WISeR Model: https://www.cms.gov/priorities/innovation/innovation-models/wiser
• HHS Office of Inspector General Compliance Guidance: https://oig.hhs.gov/compliance/

Related Orthopedic Pages

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Orthopedic Documentation Requirements

Clinical documentation checklists for authorization, coding, payment, and audit defense.

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Orthopedic Prior Authorization

Decision pathways, payer criteria, authorization workflow, peer-to-peer, and appeals.

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GoHealthcare Practice Solutions supports orthopedic and musculoskeletal organizations with prior authorization, medical necessity, payer intelligence, documentation improvement, revenue cycle performance, compliance, and workflow design.

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Developed by GoHealthcare Practice Solutions under the leadership of Pinky Maniri, Founder and Chief Executive Officer. This page is part of the GoHealthcare Orthopedic Surgery Specialty Center and the GoHealthcare MSK Specialty Procedure Library™.

Educational and operational reference only. This content does not replace physician clinical judgment, official payer policy, current CMS guidance, the current official code sets, qualified coding review, contractual review, or legal counsel. Coverage, authorization, coding, and reimbursement requirements must be independently verified before use.