AI SEO Statistics: Nursing Homes (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — a frozen buyer-intent benchmark for nursing homes.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
19% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 40 nursing homes benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 37.5% | 45% | 7.5% | 30% |
| Suggests DIY first | 42.5% | 25% | 17.5% | 28.3% |
| Names specific providers | 0% | 2.5% | 5% | 2.5% |
| Gives price or cost info | 7.5% | 15% | 17.5% | 13.3% |
| Tells to check reviews | 7.5% | 22.5% | 7.5% | 12.5% |
| Tells to verify credentials | 10% | 15% | 5% | 10% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 0% |
| Mentions local proximity | 25% | 25% | 10% | 20% |
| Gives selection criteria | 20% | 42.5% | 22.5% | 28.3% |
| Warns about red flags | 5% | 15% | 12.5% | 10.8% |
| Asks a clarifying question | 47.5% | 70% | 0% | 39.2% |
| Recommends multiple quotes | 5% | 7.5% | 0% | 4.2% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 60% |
| Suggests DIY first | 57.5% |
| Names specific providers | 92.5% |
| Gives price or cost info | 80% |
| Tells to check reviews | 72.5% |
| Tells to verify credentials | 80% |
| Mentions case studies / portfolio | 100% |
| Mentions local proximity | 65% |
| Gives selection criteria | 60% |
| Warns about red flags | 82.5% |
| Asks a clarifying question | 17.5% |
| Recommends multiple quotes | 90% |
By model
How each assistant handled Nursing Homes questions.
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.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 19% — the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant a nursing homes buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 70% (Claude) — a 70-point spread.
- Recommends hiring a professional: from 7.5% (Gemini) to 45% (Claude) — a 38-point spread.
- Suggests a DIY approach first: from 17.5% (Gemini) to 42.5% (ChatGPT) — a 25-point spread.
- Gives selection criteria: from 20% (ChatGPT) to 42.5% (Claude) — a 23-point spread.
- Tells the buyer to check reviews: from 7.5% (ChatGPT) to 22.5% (Claude) — a 15-point spread.
The widest single gap — asks a clarifying question, 70 points — means a nursing homes buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the nursing homes market.
Where they agree
The points of near-consensus in Nursing Homes.
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%).
Every behavior, measured
All twelve coded behaviors for Nursing Homes, averaged across the three models.
The behaviors AI models reproduce most often for nursing homes are asks a clarifying question (39.2% on average), recommends hiring a professional (30%) and suggests a DIY approach first (28.3%); the rarest are mentions case studies or portfolio (0%), names a specific provider (2.5%) and recommends multiple quotes (4.2%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Asks a clarifying question: 39.2% on average (ChatGPT 47.5%, Claude 70%, Gemini 0%) — a 70-point spread.
- Recommends hiring a professional: 30% on average (ChatGPT 37.5%, Claude 45%, Gemini 7.5%) — a 38-point spread.
- Suggests a DIY approach first: 28.3% on average (ChatGPT 42.5%, Claude 25%, Gemini 17.5%) — a 25-point spread.
- Gives selection criteria: 28.3% on average (ChatGPT 20%, Claude 42.5%, Gemini 22.5%) — a 23-point spread.
- Mentions local proximity: 20% on average (ChatGPT 25%, Claude 25%, Gemini 10%) — a 15-point spread.
- Gives price or cost information: 13.3% on average (ChatGPT 7.5%, Claude 15%, Gemini 17.5%) — a 10-point spread.
- Tells the buyer to check reviews: 12.5% on average (ChatGPT 7.5%, Claude 22.5%, Gemini 7.5%) — a 15-point spread.
- Warns about red flags or scams: 10.8% on average (ChatGPT 5%, Claude 15%, Gemini 12.5%) — a 10-point spread.
- Tells the buyer to verify credentials: 10% on average (ChatGPT 10%, Claude 15%, Gemini 5%) — a 10-point spread.
- Recommends multiple quotes: 4.2% on average (ChatGPT 5%, Claude 7.5%, Gemini 0%) — a 8-point spread.
- Names a specific provider: 2.5% on average (ChatGPT 0%, Claude 2.5%, Gemini 5%) — a 5-point spread.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the nursing homes buyer.
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.
Referral behavior
Do AI models name Nursing Homes providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 nursing homes answers, a specific provider was named in 2.5% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for nursing homes: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Nursing Homes questions cover.
The 40 questions behind every percentage on this page form a frozen nursing homes (healthcare services; buyer hiring decisions for this specific service) buyer-intent benchmark. 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.
How to read this
A note on the numbers.
A percentage here is the share of a model's 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific nursing homes question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
Methodology
A controlled snapshot, documented end to end.
40 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-06 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Nursing Homes (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/health/nursing-homes