The promise of AI in healthcare is immense. From diagnostic support to administrative automation, AI is increasingly being positioned as a solution to streamline complex processes.
But how effective is it when it comes to one of the most intricate regulatory and financial hurdles in U.S. healthcare—securing a new Medicare reimbursement code for an innovative medical procedure?
To explore this, I posed a straightforward yet highly technical question to several AI models:
“What is the best and fastest method to obtain a new Medicare reimbursement code for a new medical procedure?”
The goal was simple: evaluate how well AI can navigate a process that requires deep regulatory, financial, and clinical understanding.
Comparing AI Responses: Where Do They Align, and Where Do They Fall Short?
Three AI models—ChatGPT, DeepSeek, and Gemini—all provided structured, detailed responses, breaking down the process into steps. However, each model emphasized different aspects.
ChatGPT’s Response
ChatGPT provided a structured answer outlining multiple pathways, including applying for a CPT code through the AMA and seeking a New Technology Add-on Payment (NTAP) from CMS. While detailed, its response required further validation to ensure accuracy.
Example from ChatGPT: “One approach is to apply for a Category I CPT code through the American Medical Association (AMA), which requires demonstrating clinical efficacy and widespread adoption.”
Gemini’s Response
Gemini, in contrast, emphasized alternative strategies such as working with reimbursement consultants and engaging with CMS advisory committees early in the process. However, its response was more generalized and lacked references to specific CMS guidelines.
Example from Gemini: “A company can accelerate Medicare reimbursement by collaborating with reimbursement experts who specialize in navigating CMS policies.”
DeepSeek’s Response
The response from the new Chinese AI model focused more on international regulatory comparisons, mentioning reimbursement strategies in China before addressing U.S. policies. It provided useful global insights but lacked specificity regarding Medicare’s unique processes.
Example from DeepSeek: “In China, reimbursement pathways often involve negotiation with regional health authorities. For Medicare, companies may consider parallel submission strategies.”
Key Takeaways
FDA Approval Comes First AI models agree that before reimbursement, regulatory approval is required. The path depends on whether the device is entirely new (requiring a Premarket Approval, PMA) or substantially similar to existing devices (qualifying for a 510(k) clearance). While correct, this is only the starting point—approval alone does not guarantee reimbursement.
Clinical Evidence is Critical All responses emphasized the necessity of a rigorous clinical trial, typically a randomized controlled trial (RCT), to establish efficacy, safety, and cost-effectiveness. AI recognizes that Medicare requires solid data, particularly for elderly populations, but does not delve into the nuances of real-world evidence (RWE) and alternative study designs that often play a role in reimbursement decisions.
Securing CPT and HCPCS Codes The AI models correctly identify that procedure codes (CPT, issued by the AMA) and product codes (HCPCS Level II, overseen by CMS) are necessary for reimbursement. However, they simplify the timeline, implying a structured, predictable process. In reality, applications often face delays, denials, and require strategic advocacy with relevant committees.
Medicare Coverage and Reimbursement Determination AI provides a textbook breakdown: after obtaining codes, CMS determines whether Medicare will cover the procedure via National (NCD) or Local Coverage Determinations (LCD). However, AI largely overlooks the political and financial negotiations often required to drive favorable reimbursement decisions.
Final Thoughts
While AI models provide a structured starting point for understanding Medicare reimbursement, none offer a fully comprehensive or actionable roadmap.
Each response requires human validation, strategic planning, and expert navigation of regulatory and financial hurdles.
AI may be a useful tool, but in high-stakes regulatory decisions, human expertise remains irreplaceable.
Comments