AI SEO Statistics: Nursing Homes (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same nursing homes questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 45% (Claude) down to 7.5% (Gemini), a 38-point gap on an identical question set.

Across the 40 nursing homes answers it produced, ChatGPT recommended hiring a professional in 37.5% of them and suggested a DIY approach first 42.5% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 7.5% of the time. ChatGPT asked a clarifying question before answering in 47.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 10%, averaging 562 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 nursing homes answers it produced, Claude recommended hiring a professional in 45% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 15% of the time. Claude asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 15%, averaging 304 words per answer. On the remaining cues it told the buyer to check reviews in 22.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 nursing homes answers it produced, Gemini recommended hiring a professional in 7.5% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 17.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 5%, averaging 254 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, Claude is the assistant most likely to route a nursing homes buyer to a professional (45%) and Gemini the least (7.5%). ChatGPT produced the longest answers, at 562 words on average. Specific providers were named most often by Gemini (5%) — even there, roughly one answer in 20 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for nursing homes, where all three landed within a few points of each other:

• Mentions case studies or portfolio: 0% across all three models.
• Names a specific provider: 0%–5% across all three (a 5-point spread).
• Recommends multiple quotes: 0%–7.5% across all three (a 8-point spread).
• Gives price or cost information: 7.5%–17.5% across all three (a 10-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (17.5%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the nursing homes buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 12.5% of answers on average. Verifying credentials or certifications appeared in 10%. Warning about red flags or scams appeared in 10.8%.

On structuring the decision, a selection-criteria checklist showed up in 28.3% of answers on average and a recommendation to gather multiple quotes in 4.2%. The single least-reproduced protective signal for nursing homes is "recommends multiple quotes" at 4.2% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 40 questions behind every percentage on this page were drawn from real nursing homes (healthcare services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact nursing homes question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.