How CMS Creating AI Billing Codes Could Transform US Healthcare
Healthcare spending in the US keeps rising, driven partly by complex payment models and outdated workflows. CMS plans to introduce billing codes for AI services in 2026, a landmark move expected to reshape healthcare delivery and costs. But this isn’t just about new tech—it’s about how payment structures create or stifle leverage in healthcare systems. Without new payment models, AI risks just amplifying existing inefficiencies.
Conventional wisdom holds that AI adoption in healthcare will mainly cut costs and improve outcomes through automation and fraud reduction. The reality is more nuanced: the dominant fee-for-service model incentivizes more billable activities, not less. Introducing billing codes for AI without changing this underlying constraint risks spurring unnecessary care and expenses, not savings.
For example, consider the pharmacy benefit managers (PBMs) landscape. Large employers are fleeing the big three PBMs due to their complex fee inventions, seeking lower-cost alternatives with transparent service. This shift exposes the power of constraint repositioning—once the opacity and fee-multiplying levers of PBMs were clearly out of alignment with employer incentives, stakeholder behavior changed fundamentally. See how this dynamic plays out in [Why Salespeople Actually Underuse Linkedin Profiles For Closing Deals](https://thinkinleverage.com/why-salespeople-actually-underuse-linkedin-profiles-for-closing-deals/).
Similarly, AI clinical care solutions excel as complements within integrated health organizations rather than stand-alone businesses. The disruptive constraint is not AI’s clinical ability but its integration with payment and care pathways. Unlike standalone AI doctors, embedded AI tools can leverage existing infrastructure, patient data, and provider workflows, unlocking scale and improved access—precisely where leverage compounds. This mechanism echoes insights from [Why AI Actually Forces Workers To Evolve Not Replace Them](https://thinkinleverage.com/why-ai-actually-forces-workers-to-evolve-not-replace-them/).
The creation of AI billing codes by CMS represents a classic leverage play on payment infrastructure—once billing codes are available, healthcare entities can innovate around reimbursement flows and care delivery automation. But the existing fee-for-service constraint means AI could paradoxically increase healthcare costs by enabling more billable interactions rather than reducing care volume or improving efficiency.
Why Fee-for-Service Is the Hidden Constraint Blocking True AI Leverage
The US healthcare system’s reliance on fee-for-service incentivizes volume over value. Simply adding AI billing codes feeds this system without addressing how providers get paid. Even perfect AI automation aimed at fraud detection and administrative tasks can generate more billing events rather than cut them.
This is why risk adjustment changes and premium flexibility in Medicare Advantage plans matter. Without easing these constraints, healthcare firms like Humana risk piling up losses chasing membership growth under costly fee structures. Contrast that with the strategic repositioning seen when large employers abandon convoluted PBM contracts, cutting fees by choosing transparent alternatives.
How AI Complements Traditional Care by Leveraging Existing Infrastructure
AI-native clinical services falter alone due to missing scale, care coordination, and reimbursement integration. But integrated organizations deploying AI can apply clinical intelligence broadly between visits, improving access and potentially reducing episode costs. This subtle architectural advantage means the clinical value of AI compounds only within existing systems.
For example, Apple has deferred glucose monitoring launch, showing how Big Tech struggles to break into regulated healthcare without leveraging traditional player relationships and payment flows. This reminds us of the strategic complexities dissected in Why 2024 Tech Layoffs Actually Reveal Structural Leverage Failures.
Forward Looking: New Payment Structures Unlock AI’s True Healthcare Impact
The key constraint that must shift is payment model innovation. CMS billing codes for AI will catalyze product launches, but durable cost savings come only when AI is embedded within value-based care or capitated payments—not perpetuating fee-for-service incentives.
Healthcare operators, payors, and tech builders should focus on recomposing payment contracts and workflows alongside AI adoption. Regions with more advanced value-based models or integrated delivery systems hold a strategic advantage in extracting AI’s leverage. The US stands at a crossroads, and those who grasp payment constraints will outpace competitors.
"Without payment innovation, AI scales problems faster than solutions."
Related Tools & Resources
As AI integration plays a pivotal role in reshaping healthcare delivery, tools like Blackbox AI are essential for developers looking to innovate in the field of healthcare technology. By leveraging AI-powered code generation and developer tools, companies can streamline their processes and ensure that their AI solutions are both effective and compliant with emerging billing structures. Learn more about Blackbox AI →
Full Transparency: Some links in this article are affiliate partnerships. If you find value in the tools we recommend and decide to try them, we may earn a commission at no extra cost to you. We only recommend tools that align with the strategic thinking we share here. Think of it as supporting independent business analysis while discovering leverage in your own operations.
Frequently Asked Questions
What are CMS AI billing codes and when will they be introduced?
CMS AI billing codes are new codes designed to reimburse AI services in healthcare. They are planned to be introduced in 2026 to enable innovation in care delivery and reimbursement flows.
How can AI billing codes impact US healthcare costs?
AI billing codes could increase healthcare costs by enabling more billable interactions under fee-for-service models. Without payment model reform, AI may amplify existing inefficiencies instead of cutting expenses.
Why is the fee-for-service model a constraint for AI leverage in healthcare?
The fee-for-service model incentivizes volume over value, encouraging more billable services rather than cost reduction. AI adoption under this model risks increasing billing events instead of reducing care volumes.
How do integrated health organizations benefit from AI differently than standalone AI providers?
Integrated organizations can embed AI in clinical workflows, leveraging existing infrastructure and patient data, unlocking scale and improved access. Standalone AI providers lack this integration, limiting their clinical impact.
What role do payment models like Medicare Advantage play in AI healthcare adoption?
Payment models with risk adjustment and premium flexibility, like Medicare Advantage, can ease constraints and enable strategic AI adoption. Conversely, traditional fee-for-service plans risk losses if AI adoption focuses solely on volume growth.
Can you give an example of how large employers respond to complex healthcare fees?
Large employers are abandoning the big three pharmacy benefit managers due to complex fees, opting for transparent alternatives which reduce costs, demonstrating how payment transparency can change stakeholder behavior.
Why did Apple delay its glucose monitoring launch?
Apple deferred its glucose monitoring launch due to challenges breaking into regulated healthcare without leveraging traditional provider relationships and payment flows, highlighting the importance of existing infrastructure.
What must change to unlock AI’s true leverage in healthcare?
The key is innovating payment models to move beyond fee-for-service toward value-based or capitated payments. Embedding AI within these models enables durable cost savings and better care efficiency.