Original research · 2026-07 edition

AI SEO Statistics: Hospice (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

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The question bank

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

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

How do I know if my mom is ready for hospice or if we should keep trying treatments?
What is the actual difference between palliative care and hospice care?
Does insurance or Medicare cover 100% of hospice costs or will there be surprise bills?
Can we do hospice at home if I am the only caregiver and I work full-time?
What specific questions should I ask when interviewing different hospice agencies?
Is it better to choose a non-profit hospice over a for-profit one?
How quickly can a hospice team start once we make the decision to hire them?
What happens if my loved one starts getting better while on hospice care?
Show all 40 questions
Do they provide the hospital bed and oxygen or do I have to rent those separately?
Can I still take my dad to his regular primary care doctor while he is in hospice?
What are the red flags I should look for during the first week of hospice service?
Are there hospice facilities where he can stay or is it always done at home?
How often does a nurse actually come to the house for a routine visit?
My grandmother has end-stage dementia; at what point does she qualify for hospice?
If there is a crisis at 3 AM on a Saturday who do I call and how fast do they respond?
Can we hire a private nurse alongside hospice if we want 24/7 bedside care?
Does hospice help with bathing and changing my parent or is that mostly on the family?
What kind of medications do they use to manage pain and the death rattle symptoms?
Can I switch hospice providers if I am not happy with the current team?
What exactly is respite care in hospice and how often are we allowed to use it?
Do hospice workers stay in the house all day or just visit for an hour at a time?
How do I explain to my dad that we are calling hospice without making him feel like we are giving up?
Is morphine always required in hospice or are there other options for pain management?
Who pays for the medications related to the terminal illness once hospice starts?
Can a person stay on hospice for longer than six months if they don't pass away?
What happens if we decide we want to go back to the hospital for a new experimental treatment?
Do hospice agencies provide spiritual support if our family is not religious?
How do I find the highest-rated hospice providers in my specific zip code?
Does hospice help with the funeral arrangements or what to do immediately after the person passes?
What is the difference between routine home care and continuous care in a hospice contract?
Are there specific hospices that specialize in pediatric care for children?
Do they provide help for the family's mental health and grief counseling after the death?
Can we get hospice if my mom lives in an assisted living facility instead of her own house?
What are the signs that a hospice nurse is spread too thin or not doing their job correctly?
Is it true that hospice speeds up the dying process with the medication they give?
How do I coordinate between the hospice team and the facility staff where my dad lives?
What should be included in the comfort kit they bring to the house?
Can I get hospice care if the patient does not have a designated primary caregiver at home?
What are the typical out-of-pocket costs for things like adult diapers and nutritional shakes?
Do I need a formal doctor's referral to start talking to hospice agencies about their services?

Model by model

15-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 hospice buyers.

Behavior rates across 40 hospice buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional70%60%33%50%
Suggests DIY first8%5%3%93%
Names specific providers3%8%10%93%
Gives price or cost info3%15%23%78%
Tells to check reviews8%8%0%90%
Tells to verify credentials10%5%0%90%
Mentions case studies / portfolio3%0%0%98%
Mentions local proximity35%18%13%58%
Gives selection criteria23%15%5%73%
Warns about red flags3%3%5%95%
Asks a clarifying question58%75%5%20%
Recommends multiple quotes3%5%0%93%

By model

How each assistant handled Hospice questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same hospice questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 70% (ChatGPT) down to 32.5% (Gemini), a 38-point gap on an identical question set.

Across the 40 hospice answers it produced, ChatGPT recommended hiring a professional in 70% of them and suggested a DIY approach first 7.5% 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 2.5% of the time. ChatGPT asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 10%, averaging 438 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 2.5%, and framed the choice around local proximity in 35%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 2.5%.

Across the 40 hospice answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 7.5% of answers (about 0.5 distinct providers per answer) and included price or cost information 15% of the time. Claude asked a clarifying question before answering in 75% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 5%, averaging 283 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 17.5%; a selection-criteria checklist appeared in 15% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 hospice answers it produced, Gemini recommended hiring a professional in 32.5% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 22.5% of the time. Gemini asked a clarifying question before answering in 5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 0%, averaging 293 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a hospice buyer to a professional (70%) and Gemini the least (32.5%). ChatGPT produced the longest answers, at 438 words on average. Specific providers were named most often by Gemini (10%) — even there, roughly one answer in 10 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 15.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a hospice buyer happens to ask matters most:

  • Asks a clarifying question: from 5% (Gemini) to 75% (Claude) — a 70-point spread.
  • Recommends hiring a professional: from 32.5% (Gemini) to 70% (ChatGPT) — a 38-point spread.
  • Mentions local proximity: from 12.5% (Gemini) to 35% (ChatGPT) — a 23-point spread.
  • Gives price or cost information: from 2.5% (ChatGPT) to 22.5% (Gemini) — a 20-point spread.
  • Gives selection criteria: from 5% (Gemini) to 22.5% (ChatGPT) — a 18-point spread.

The widest single gap — asks a clarifying question, 70 points — means a hospice 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 hospice market.

Where they agree

The points of near-consensus in Hospice.

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

  • Mentions case studies or portfolio: 0%–2.5% across all three (a 3-point spread).
  • Warns about red flags or scams: 2.5%–5% across all three (a 3-point spread).
  • Suggests a DIY approach first: 2.5%–7.5% across all three (a 5-point spread).
  • Recommends multiple quotes: 0%–5% across all three (a 5-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 97.5% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

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

The behaviors AI models reproduce most often for hospice are recommends hiring a professional (54.2% on average), asks a clarifying question (45.8%) and mentions local proximity (21.7%); the rarest are mentions case studies or portfolio (0.8%), recommends multiple quotes (2.5%) and warns about red flags or scams (3.3%). 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:

  • Recommends hiring a professional: 54.2% on average (ChatGPT 70%, Claude 60%, Gemini 32.5%) — a 38-point spread.
  • Asks a clarifying question: 45.8% on average (ChatGPT 57.5%, Claude 75%, Gemini 5%) — a 70-point spread.
  • Mentions local proximity: 21.7% on average (ChatGPT 35%, Claude 17.5%, Gemini 12.5%) — a 23-point spread.
  • Gives selection criteria: 14.2% on average (ChatGPT 22.5%, Claude 15%, Gemini 5%) — a 18-point spread.
  • Gives price or cost information: 13.3% on average (ChatGPT 2.5%, Claude 15%, Gemini 22.5%) — a 20-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 10%) — a 8-point spread.
  • Suggests a DIY approach first: 5% on average (ChatGPT 7.5%, Claude 5%, Gemini 2.5%) — a 5-point spread.
  • Tells the buyer to check reviews: 5% on average (ChatGPT 7.5%, Claude 7.5%, Gemini 0%) — a 8-point spread.
  • Tells the buyer to verify credentials: 5% on average (ChatGPT 10%, Claude 5%, Gemini 0%) — a 10-point spread.
  • Warns about red flags or scams: 3.3% on average (ChatGPT 2.5%, Claude 2.5%, Gemini 5%) — a 3-point spread.
  • Recommends multiple quotes: 2.5% on average (ChatGPT 2.5%, Claude 5%, Gemini 0%) — a 5-point spread.
  • Mentions case studies or portfolio: 0.8% on average (ChatGPT 2.5%, Claude 0%, Gemini 0%) — a 3-point spread.

Trust signals

How well the models protect the hospice buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the hospice buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5% of answers on average. Verifying credentials or certifications appeared in 5%. Warning about red flags or scams appeared in 3.3%.

On structuring the decision, a selection-criteria checklist showed up in 14.2% of answers on average and a recommendation to gather multiple quotes in 2.5%. The single least-reproduced protective signal for hospice is "recommends multiple quotes" at 2.5% 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 Hospice providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 hospice answers, a specific provider was named in 6.7% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for hospice: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Hospice questions cover.

The 40 questions behind every percentage on this page were drawn from real hospice (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 hospice 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 hospice 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 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 2026-07-06, 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 →