Original research · 2026-07 edition

AI SEO Statistics: Telehealth (2026-07 edition)

38 questions · 114 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 telehealth.

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

What is the average cost of a one-time virtual doctor visit if I do not have health insurance?
Can I get a refill for my blood pressure medication through a video call or do I have to go in person?
Is it possible for a telehealth doctor to accurately diagnose a skin rash just from a high-resolution photo?
How do I verify if a virtual healthcare platform is actually HIPAA compliant and keeps my records private?
I have a high fever and a sore throat; is it better to go to urgent care or try a telehealth app first?
Do online doctors usually accept HSA or FSA cards for payment of the consultation fee?
What kind of equipment or tech do I need at home to have a successful virtual physical therapy session?
Can a telehealth provider order blood work at a lab near me, or do they only do consultations?
Show all 38 questions
Are there specific states where I cannot use telehealth for mental health prescriptions due to local laws?
How long does a typical virtual appointment last compared to a standard in-office visit?
What are the red flags I should look for when choosing a subscription-based online medical service?
Can I get a doctor's note for work through a telehealth visit for a 24-hour stomach bug?
Is virtual therapy actually as effective as sitting in a physical office with a counselor?
If I use a telehealth service, will they share my visit notes with my regular primary care doctor automatically?
What happens if the video connection cuts out during my appointment; will I still be charged the full price?
Are there specific telehealth services that specialize in pediatric care for late-night emergencies?
Can an online doctor prescribe antibiotics for a UTI without requiring a physical urine sample?
How do I know if the person on the other end of the video call is a board-certified doctor?
Is it cheaper to pay a monthly membership fee for telehealth or just pay a flat fee per visit?
What should I do if a telehealth doctor tells me I need to go to the ER immediately during our call?
Can I use a telehealth service while I am traveling out of state or in a different country?
Do virtual doctors have the authority to refer me to a local specialist like a cardiologist?
How do I prepare my toddler for their first virtual pediatrician appointment so they stay calm?
Are there any specific medical conditions that are strictly prohibited from being treated via telehealth?
Why do some telehealth platforms charge a convenience fee on top of the actual consultation price?
Can I get a second opinion on a surgery through a virtual consult if I upload my MRI scans?
How quickly can I usually get an appointment on a telehealth app on a Saturday night?
Is my insurance company required to cover telehealth visits at the same rate as in-person visits?
What are the pros and cons of using a big national telehealth provider versus my local doctor's portal?
Can a virtual doctor help me manage a chronic condition like diabetes or asthma over the long term?
Do I need a high-speed internet connection, or can I do a telehealth visit over a 4G phone signal?
Are there telehealth services that focus specifically on men's or women's hormonal health and HRT?
What is the process for getting a prescription sent to my local pharmacy after a virtual visit concludes?
How can I tell if a telehealth site is a legitimate medical practice versus a low-quality pill mill?
Can I have a joint telehealth session with my spouse and a counselor from two different locations?
Is it possible to get a disability claim or FMLA paperwork signed through a telehealth provider?
What medical tools should I have ready, like a thermometer or BP cuff, before my virtual appointment?
Do telehealth doctors have access to my existing electronic health records from other hospital systems?

Model by model

19-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 telehealth buyers.

Behavior rates across 38 telehealth buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional47%40%40%71%
Suggests DIY first13%8%0%82%
Names specific providers11%32%47%58%
Gives price or cost info5%8%13%92%
Tells to check reviews3%8%0%92%
Tells to verify credentials21%16%13%68%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity29%34%16%61%
Gives selection criteria32%34%18%50%
Warns about red flags3%8%11%87%
Asks a clarifying question68%68%0%8%
Recommends multiple quotes5%3%0%92%

By model

How each assistant handled Telehealth questions.

Reading the 114 answers model by model shows how differently the three assistants treat the same telehealth questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 47.4% (ChatGPT) down to 39.5% (Claude), a 8-point gap on an identical question set.

Across the 38 telehealth answers it produced, ChatGPT recommended hiring a professional in 47.4% of them and suggested a DIY approach first 13.2% of the time. It named a specific provider in 10.5% of answers (about 0.8 distinct providers per answer) and included price or cost information 5.3% of the time. ChatGPT asked a clarifying question before answering in 68.4% of cases, warned about red flags or scams in 2.6%, and told the buyer to verify credentials in 21.1%, averaging 417 words per answer. On the remaining cues it told the buyer to check reviews in 2.6%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 28.9%; a selection-criteria checklist appeared in 31.6% of its answers and a recommendation to gather multiple quotes in 5.3%.

Across the 38 telehealth answers it produced, Claude recommended hiring a professional in 39.5% of them and suggested a DIY approach first 7.9% of the time. It named a specific provider in 31.6% of answers (about 1.4 distinct providers per answer) and included price or cost information 7.9% of the time. Claude asked a clarifying question before answering in 68.4% of cases, warned about red flags or scams in 7.9%, and told the buyer to verify credentials in 15.8%, averaging 275 words per answer. On the remaining cues it told the buyer to check reviews in 7.9%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 34.2%; a selection-criteria checklist appeared in 34.2% of its answers and a recommendation to gather multiple quotes in 2.6%.

Across the 38 telehealth answers it produced, Gemini recommended hiring a professional in 39.5% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 47.4% of answers (about 2 distinct providers per answer) and included price or cost information 13.2% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10.5%, and told the buyer to verify credentials in 13.2%, averaging 292 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 15.8%; a selection-criteria checklist appeared in 18.4% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a telehealth buyer to a professional (47.4%) and Claude the least (39.5%). ChatGPT produced the longest answers, at 417 words on average. Specific providers were named most often by Gemini (47.4%) — even there, roughly one answer in 2 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 18.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a telehealth buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 68.4% (ChatGPT) — a 68-point spread.
  • Names a specific provider: from 10.5% (ChatGPT) to 47.4% (Gemini) — a 37-point spread.
  • Mentions local proximity: from 15.8% (Gemini) to 34.2% (Claude) — a 18-point spread.
  • Gives selection criteria: from 18.4% (Gemini) to 34.2% (Claude) — a 16-point spread.
  • Suggests a DIY approach first: from 0% (Gemini) to 13.2% (ChatGPT) — a 13-point spread.

The widest single gap — asks a clarifying question, 68 points — means a telehealth 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 telehealth market.

Where they agree

The points of near-consensus in Telehealth.

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

  • Mentions case studies or portfolio: 0% across all three models.
  • Recommends multiple quotes: 0%–5.3% across all three (a 5-point spread).
  • Recommends hiring a professional: 39.5%–47.4% across all three (a 8-point spread).
  • Gives price or cost information: 5.3%–13.2% across all three (a 8-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" (7.9%).

Every behavior, measured

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

The behaviors AI models reproduce most often for telehealth are asks a clarifying question (45.6% on average), recommends hiring a professional (42.1%) and names a specific provider (29.8%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (2.6%) and tells the buyer to check reviews (3.5%). Each figure below is the share of a model's 38 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: 45.6% on average (ChatGPT 68.4%, Claude 68.4%, Gemini 0%) — a 68-point spread.
  • Recommends hiring a professional: 42.1% on average (ChatGPT 47.4%, Claude 39.5%, Gemini 39.5%) — a 8-point spread.
  • Names a specific provider: 29.8% on average (ChatGPT 10.5%, Claude 31.6%, Gemini 47.4%) — a 37-point spread.
  • Gives selection criteria: 28.1% on average (ChatGPT 31.6%, Claude 34.2%, Gemini 18.4%) — a 16-point spread.
  • Mentions local proximity: 26.3% on average (ChatGPT 28.9%, Claude 34.2%, Gemini 15.8%) — a 18-point spread.
  • Tells the buyer to verify credentials: 16.7% on average (ChatGPT 21.1%, Claude 15.8%, Gemini 13.2%) — a 8-point spread.
  • Gives price or cost information: 8.8% on average (ChatGPT 5.3%, Claude 7.9%, Gemini 13.2%) — a 8-point spread.
  • Suggests a DIY approach first: 7% on average (ChatGPT 13.2%, Claude 7.9%, Gemini 0%) — a 13-point spread.
  • Warns about red flags or scams: 7% on average (ChatGPT 2.6%, Claude 7.9%, Gemini 10.5%) — a 8-point spread.
  • Tells the buyer to check reviews: 3.5% on average (ChatGPT 2.6%, Claude 7.9%, Gemini 0%) — a 8-point spread.
  • Recommends multiple quotes: 2.6% on average (ChatGPT 5.3%, Claude 2.6%, Gemini 0%) — 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 telehealth buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the telehealth buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 3.5% of answers on average. Verifying credentials or certifications appeared in 16.7%. Warning about red flags or scams appeared in 7%.

On structuring the decision, a selection-criteria checklist showed up in 28.1% of answers on average and a recommendation to gather multiple quotes in 2.6%. The single least-reproduced protective signal for telehealth is "recommends multiple quotes" at 2.6% 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 Telehealth providers?

For service providers the decisive question is whether these systems name anyone at all. Across 114 telehealth answers, a specific provider was named in 29.8% of responses on average — roughly 1.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for telehealth: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 38 Telehealth questions cover.

The 38 questions behind every percentage on this page were drawn from real telehealth (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 telehealth 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 38 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 telehealth 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.

38 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 →