When a patient asks AI for a doctor, which practices get named?
Usually, no independent one. We sampled 200 independent practices at random and ran nearly 5,000 patient-realistic prompts through ChatGPT. It named zero of them.
This is the running record of what large language models actually say when people look for care – which practices surface, which sources the models trust, and what the visible practices do differently. Built from our own measurement corpus, shared openly.
- Practices
- 200
- Prompts
- ~4,950
- Specialties
- 18
- Metros
- 20
- Model
- GPT-5.5
- Run
- May 2026
ChatGPT mentioned and cited 0 of 200 randomly-sampled independent practices.
The findings
Why ChatGPT doesn’t mention your practice
The 0-of-200 result, the five on-site signals almost no practice has, and why your specialty and metro set your ceiling.
Read the on-site findingsWhere ChatGPT gets its answers
An interactive map of 38,178 citation events – the hospital rosters, associations, and magazines the model actually pulls from, pivotable by specialty.
Explore the citation mapHow we measured it
Every figure on this site comes from one measurement corpus, not estimates.
We drew a stratified random sample of independent practices from the NPPES registry across 18 specialties and 20 metros, then ran a fixed battery of patient-realistic prompts – scoped to each practice’s specialty and metro – through GPT-5.5 via the OpenAI API, with multiple runs per query to absorb variance. We separate a mention (your name in the answer a patient reads) from a citation (a URL the model pulls from), and anchor on mentions wherever the data supports it. Single-platform, US-only, correlational. Behavior on Perplexity, Gemini, and Google AI Overviews may differ.