Original research · 2026-07 edition

AI SEO Statistics: Hospital (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in hospital.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

Should I go to the ER for a fever of 103 that won't go down with meds, or can it wait until morning?
How do I compare the infection rates and patient safety scores of the two main hospitals in my area?
What's the typical price difference for an outpatient gallbladder surgery versus getting it done as an inpatient?
I don't have insurance; which local hospitals have the best financial assistance or charity care programs?
Is it better to choose a specialized heart hospital or a general hospital for a valve replacement?
What are the red flags I should look for when choosing a hospital for my elderly mother's hip surgery?
Can I request a specific surgeon if I'm being admitted through the emergency room?
How do I find out which hospital in my city has the lowest NICU mortality rates?
Show all 15 questions
My local hospital is a teaching hospital; does that mean students will be doing my surgery?
What are the pros and cons of a private boutique hospital versus a large university medical center for elective procedures?
How can I get an itemized bill from a hospital and what should I look for to spot overcharging?
If I need a scheduled C-section, how early should I start touring hospitals to see their maternity wards?
Is there a way to check if a hospital is in-network for my specific plan before I show up for an emergency?
What's the difference in care quality between a non-profit hospital and a for-profit one?
My child swallowed a small toy; should I go to a general ER or find a dedicated children's hospital?

Model by model

22-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about hospital buyers.

Behavior rates across 15 hospital buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional40%33%13%67%
Suggests DIY first27%40%27%73%
Names specific providers7%7%7%87%
Gives price or cost info0%13%13%80%
Tells to check reviews20%27%13%67%
Tells to verify credentials27%27%0%60%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity33%53%47%40%
Gives selection criteria33%67%47%40%
Warns about red flags13%13%0%80%
Asks a clarifying question40%73%7%33%
Recommends multiple quotes13%7%0%87%

By model

How each assistant handled Hospital questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same hospital questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 40% (ChatGPT) down to 13.3% (Gemini), a 27-point gap on an identical question set.

Across the 15 hospital answers it produced, ChatGPT recommended hiring a professional in 40% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 0% of the time. ChatGPT asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 555 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 hospital answers it produced, Claude recommended hiring a professional in 33.3% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 298 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 53.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 hospital answers it produced, Gemini recommended hiring a professional in 13.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 13.3% of the time. Gemini asked a clarifying question before answering in 6.7% of cases, warned about red flags or scams in 0%, and told the buyer to verify credentials in 0%, averaging 246 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a hospital buyer to a professional (40%) and Gemini the least (13.3%). ChatGPT produced the longest answers, at 555 words on average. Specific providers were named most often by ChatGPT (6.7%) — even there, roughly one answer in 15 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 21.5 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a hospital buyer happens to ask matters most:

  • Asks a clarifying question: from 6.7% (Gemini) to 73.3% (Claude) — a 67-point spread.
  • Gives selection criteria: from 33.3% (ChatGPT) to 66.7% (Claude) — a 33-point spread.
  • Recommends hiring a professional: from 13.3% (Gemini) to 40% (ChatGPT) — a 27-point spread.
  • Tells the buyer to verify credentials: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.
  • Mentions local proximity: from 33.3% (ChatGPT) to 53.3% (Claude) — a 20-point spread.

The widest single gap — asks a clarifying question, 67 points — means a hospital 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 hospital market.

Where they agree

The points of near-consensus in Hospital.

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

  • Names a specific provider: 6.7% across all three models.
  • Mentions case studies or portfolio: 0% across all three models.
  • Suggests a DIY approach first: 26.7%–40% across all three (a 13-point spread).
  • Gives price or cost information: 0%–13.3% across all three (a 13-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" (33.3%).

Every behavior, measured

All twelve coded behaviors for Hospital, averaged across the three models.

The behaviors AI models reproduce most often for hospital are gives selection criteria (48.9% on average), mentions local proximity (44.4%) and asks a clarifying question (40%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (6.7%) and names a specific provider (6.7%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 48.9% on average (ChatGPT 33.3%, Claude 66.7%, Gemini 46.7%) — a 33-point spread.
  • Mentions local proximity: 44.4% on average (ChatGPT 33.3%, Claude 53.3%, Gemini 46.7%) — a 20-point spread.
  • Asks a clarifying question: 40% on average (ChatGPT 40%, Claude 73.3%, Gemini 6.7%) — a 67-point spread.
  • Suggests a DIY approach first: 31.1% on average (ChatGPT 26.7%, Claude 40%, Gemini 26.7%) — a 13-point spread.
  • Recommends hiring a professional: 28.9% on average (ChatGPT 40%, Claude 33.3%, Gemini 13.3%) — a 27-point spread.
  • Tells the buyer to check reviews: 20% on average (ChatGPT 20%, Claude 26.7%, Gemini 13.3%) — a 13-point spread.
  • Tells the buyer to verify credentials: 17.8% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 0%) — a 27-point spread.
  • Gives price or cost information: 8.9% on average (ChatGPT 0%, Claude 13.3%, Gemini 13.3%) — a 13-point spread.
  • Warns about red flags or scams: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
  • Recommends multiple quotes: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the hospital buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the hospital buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 20% of answers on average. Verifying credentials or certifications appeared in 17.8%. Warning about red flags or scams appeared in 8.9%.

On structuring the decision, a selection-criteria checklist showed up in 48.9% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for hospital is "recommends multiple quotes" at 6.7% 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 Hospital providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 hospital answers, a specific provider was named in 6.7% 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 hospital: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Hospital questions cover.

The 15 questions behind every percentage on this page were drawn from real hospital (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 hospital 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 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific hospital 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.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →