This is an opinion section. 

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I'm an insurance geek, not just the Medicare Maven!

I love exploring new technologies and AI is exciting and controversial. 

 

Large Language Models (LLMs), including ChatGPT, Gemini and Claude can struggle with complex regulatory subjects such as Medicare. While Medicare is a federal program, it operates within a complicated framework of federal regulations, state insurance laws, annual CMS updates, and insurance carrier policies.

 

The challenge isn't simply finding information; it's understanding which rules apply to a particular person, insurance product, state, and point in time. As a result, AI-generated Medicare information can be incomplete, outdated, or confidently incorrect.

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Medicare is an especially challenging subject for Large Language Models (LLMs) because the rules are complex, frequently changing, and highly dependent on individual circumstances.

The problem isn't simply the volume of regulations. It's how those regulations interact.

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Why LLMs can struggle with Medicare

1. Federal versus State regulations. Medicare is a federal program, but Medicare Supplement (Medigap) insurance is also subject to state insurance laws. A correct answer about guaranteed-issue rights in one state may be incorrect in another.

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2. Different rules for different Medicare products. Original Medicare, Medicare Advantage, Part D, and Medigap operate under different regulatory frameworks. LLMs sometimes confuse rules that apply to one product with those governing another. This gets extremely critical when decisions are made that can cost a consumer money, over long periods of time, such as late enrollment penalties. 

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3. Regulations change frequently. CMS issues annual updates, guidance, and regulatory changes. An LLM may provide an answer that was accurate in 2024 but is no longer accurate in 2026 or 2027.

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4. Exceptions to the rules. Medicare is full of exceptions, particularly involving enrollment periods, guaranteed-issue rights, employer coverage, and special circumstances. LLMs can identify a general rule while overlooking an exception that completely changes the answer. Area disaster declarations that enable special enrollment periods are especially problematic!

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5. Conflicting sources of information. CMS regulations, Medicare.gov explanations, state insurance department guidance, insurance contracts, and carrier policies don't always address questions in the same level of detail. An LLM may fail to distinguish between a legal requirement, an administrative practice, and a carrier's policy.

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6. Confident answers that are wrong. Perhaps the greatest concern is that LLMs can present inaccurate or incomplete information with considerable confidence. The answer may sound authoritative even when it overlooks critical regulation.

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One important distinction

LLMs are often excellent at explaining Medicare concepts, but they are less reliable at independently determining how specific regulations apply to an individual's situation.

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As a 23-year insurance veteran, with a geeky brain and experience in Medicare and personal caretaking, AI can be useful for my research, comparing regulations, and simplifying complicated explanations.

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But I would never use an LLM's answer alone as the final authority on a complicated Medicare eligibility, enrollment, or insurance rights question. I know enough to know when there is doubt, but the average consumer may not realize the error, as the answer sounds so very authoritative!

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My suggested rule if you use AI to identify and explain policies or applicable regulations, is to verify  answers against current CMS guidance, state law, and the actual insurance contract or...ask me, or another experienced and certified, fully licensed insurance professional for clarification. 

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