AI in Coding and Claims 2026 - Legal Considerations

Artificial intelligence is no longer sitting quietly in the background of the revenue cycle. It is assigning codes, shaping clinical narratives, flagging claims, and influencing denial logic in ways that directly impact reimbursement and compliance exposure. In 2026, the real issue is not whether AI can improve efficiency. The issue is whether those AI-driven decisions can withstand audit, appeal, and legal scrutiny. This session takes a direct look at how AI is being used in coding and claims today, and what that means when those decisions are challenged by payers, regulators, or opposing counsel.

As adoption accelerates, so does oversight. Agencies like the Office of Inspector General are paying closer attention to technology-enabled billing practices, and enforcement risk under the False Claims Act continues to expand. The presence of AI does not reduce liability. If anything, it raises new questions around authorship, validation, and accountability. If an algorithm assigns a higher-level code, what supports that decision? If a claim is denied based on automated logic, where is the defensible rationale? This session breaks down these questions in practical terms, grounded in real audit findings and litigation trends that are already surfacing across the industry.

AI may assist in making decisions, but it does not absorb the risk. Every coded service and every submitted claim must still be supported, explainable, and defensible. If an organization cannot clearly stand behind an AI-driven outcome, it becomes a liability rather than an advantage.

• Identify how AI is currently used across coding and claims workflows, including code assignment, documentation support, and denial logic, and assess where these applications introduce compliance risk
• Evaluate legal accountability for AI-driven decisions, including how liability is applied under the False Claims Act and related enforcement actions
• Analyze regulatory expectations and enforcement trends related to AI use in healthcare billing, including oversight from the Office of Inspector General
• Assess documentation integrity and determine whether AI-influenced clinical content meets coding guidelines, medical necessity requirements, and audit standards
• Develop strategies to ensure audit trails clearly support and explain AI-influenced coding and claims decisions
• Recognize high-risk scenarios, including unsupported upcoding, undercoding, and misaligned automated claim edits or denial logic
• Evaluate AI-driven denial patterns to determine alignment with payer policies and defensibility in appeals
• Identify risks related to bias, data integrity, and transparency within AI models and their impact on coding accuracy
• Apply practical methods to evaluate AI tools for compliance before implementation and during ongoing use
• Design internal controls and governance frameworks to manage and mitigate AI-related compliance risk
• Formulate effective responses to audits, payer disputes, and legal challenges involving AI-supported claims
• Integrate real-world lessons and case examples to strengthen the defensibility of AI-influenced coding and claims decisions.

• Current use of AI across coding and claims workflows, including code assignment, documentation summarization, and denial logic
• Legal accountability for AI-driven decisions and who owns the outcome when errors occur
• Enforcement trends tied to AI use, including scrutiny under the False Claims Act
• Regulatory oversight expectations from agencies such as the Office of Inspector General
• Documentation integrity risks when AI contributes to or generates clinical content
• Audit trail requirements and the ability to explain how AI-influenced decisions were made
• High-risk scenarios, including unsupported upcoding, undercoding, and misaligned claim edits
• AI-driven denial patterns and whether they align with payer policies and medical necessity standards
• Bias and data integrity concerns within AI models and their impact on coding accuracy
• Evaluating AI tools for compliance before implementation and during ongoing use
• Internal controls and governance strategies to manage AI-related risk
• Responding to audits, payer disputes, and legal challenges involving AI-supported claims
• Real-world examples of AI-related compliance failures and lessons learned
• Practical approaches to ensure AI outputs remain defensible in audits, appeals, and litigation.

Artificial intelligence has moved quickly from a back-end efficiency tool to an active participant in coding and claims decision-making, influencing everything from code selection to denial logic and payment outcomes. What started as automation for repetitive tasks is now shaping clinical narratives, medical necessity determinations, and audit triggers, often without clear visibility into how those decisions are made. At the same time, regulators and enforcement agencies, including the Office of Inspector General, are paying closer attention to whether these tools align with established coding guidelines, payer policies, and documentation standards. The legal pressure is building under frameworks like the False Claims Act, where liability does not disappear just because a machine was involved. This creates a new reality where organizations must not only use AI effectively but also prove that its outputs are accurate, supported, and defensible under audit and legal scrutiny.

AI is already influencing coding decisions, claim edits, and denial logic, but most organizations are not prepared to defend those decisions when they are challenged. This session cuts through the hype and focuses on where the real exposure lives. It shows how AI-driven outputs are being evaluated in audits, questioned by payers, and dissected in legal settings tied to enforcement risks under frameworks like the False Claims Act and scrutiny from the OIG. Attendees will walk away with a clear understanding of how to spot weak points in AI-supported coding and claims workflows, how to strengthen documentation and audit trails, and how to respond when those decisions are challenged. The value is simple: protect revenue, reduce legal risk, and make sure every AI-influenced claim can stand up when it matters most.

• Coders
• Auditors
• Billers
• Educators
• Consultants
• Health Information Management Professionals
• Revenue Cycle Management Professionals
• Revenue Integrity
• Medical Providers of all specialties
• Physician Advisers
• Compliance Officers/Committees