AI answered patients’ questions and cited everyone but the clinic.
A clinic group found that AI Overviews answered patient treatment questions by citing national publishers, never the clinic. Beeyacorp rebuilt key content into clinician-reviewed answer blocks with medical schema and local entity signals. The clinic became cited for treatment-plus-location queries, lifting AI referrals 41% across 23 tracked queries.
A clinic group found that AI Overviews answered patient treatment questions by citing national publishers, never the clinic. Beeyacorp rebuilt key content into clinician-reviewed answer blocks with medical schema and local entity signals. The clinic became cited for treatment-plus-location queries, lifting AI referrals 41% across 23 tracked queries.
AI answered patients’ questions and cited everyone but the clinic
As patients increasingly asked AI assistants about treatments and who to see, the clinic was invisible in those answers. Its content wasn’t structured for extraction, and AI systems defaulted to national publishers, so the clinic missed a growing channel of consultation-ready patients.
Objectives
- Earn citations in AI answers. Get cited for relevant treatment queries.
- Structure content for extraction. Make it easy for AI systems to extract and cite.
- Add trusted signals. Build the medical and local signals AI systems rely on.
- Grow consultation-ready referrals. Turn AI search into a real referral channel.
What the account revealed under inspection
This began, like every engagement, with a full audit. Each finding below is backed by evidence from inside the account.
| Area | Finding |
|---|---|
| GEO | Content was not structured as extractable, citable answers. |
| Schema | No medical schema to support machine parsing. |
| Entities | Weak local entity signals for treatment-plus-location queries. |
| Content | Marketing prose rather than direct, quotable answers. |
Content buried facts in prose AI systems could not easily extract, missing medical schema and entity signals reduced trust and parseability, and without structured answers, AI defaulted to national publishers.
How we fixed it
Make the clinic the clearest citable source. We rebuilt key content into clinician-reviewed answer blocks, added medical schema, and strengthened local entity signals, so AI systems could extract, trust, and cite the clinic for treatment-plus-location queries.
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What we did
- Rebuilt content. Turned it into clinician-reviewed answer blocks.
- Added medical schema. Supported machine parsing across treatment pages.
- Strengthened local entity signals. Reinforced trust for treatment-plus-location queries.
- Tracked citations. Monitored results across AI engines by query.
How it was sequenced
| Phase | Timeframe | What Happened |
|---|---|---|
| Baseline | Weeks 1–2 | AI visibility measured across tracked queries |
| Restructure | Weeks 2–8 | Answer blocks, schema, entity signals built |
| Verify | Weeks 6–12 | Citations confirmed across engines |
| Expand | Ongoing | Coverage extended to more queries |
Becoming the cited source for treatment questions
Pulled directly from the account: the numbers below reflect what the engagement actually delivered.
| Metric | Before | After |
|---|---|---|
| Cited queries | 0 | 23 |
| AI referrals | Baseline | +41% |
| Treatment pages | Prose | Answer-structured |
| Clinician review | None | 100% |
| Medical schema | None | Deployed |
AI citation depends on extractable, structured, trustworthy content, and medical schema plus local entity signals earn citations for treatment-plus-location queries, with clinician review strengthening both accuracy and the trust AI systems reward. As patients turn to AI assistants for treatment guidance, clinics need content structured for extraction and citation, a pattern we see across our healthcare marketing clients.
Frequently Asked Questions
It's optimising content so AI systems like ChatGPT, Perplexity, and Google AI Overviews can extract, trust, and cite it in their answers. Unlike traditional SEO, the goal is being quoted inside the answer, not just ranked in a list of links.
Because its content buried facts inside marketing prose that AI systems couldn't easily extract, and it lacked the medical schema and entity signals those systems trust. So AI defaulted to national publishers whose content was more structured.
Answer blocks present key facts as direct, quotable statements a language model can lift cleanly. Structuring clinician-reviewed content this way made the clinic's information easy to extract and cite for specific treatment queries.
No. No one can force an AI system's output. What can be improved is eligibility: making content extractable, trustworthy, and well-supported by schema and entity signals, which is what lifted this clinic's citations across 23 tracked queries.
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