Be the Practice AI Recommends
Patients have started asking AI assistants the questions they used to ask search engines: who treats this near me, is this symptom urgent, which practice takes my insurance. The answers come back as two or three names with reasons attached. Whether your practice is one of them depends on sources you can influence: your profiles, your provider data, the review platforms and directories the models quote, and whether a model can even tell your clinicians are real credentialed people.
What changes at this scale
Health answers are held to a stricter standard
Models hedge hard on medical topics and lean on sources they can verify: credential databases, hospital affiliations, established directories, review platforms. A practice that is thin or inconsistent across those gets left out of answers entirely, not ranked lower.
Wrong answers are a patient safety and revenue problem
A model stating an outdated address, a closed location, a departed provider or the wrong accepted insurance sends patients elsewhere silently. The first scan almost always finds at least one of these, and correcting the sources fixes it.
Provider entities carry the practice
AI engines resolve trust through people: named clinicians with verifiable credentials, affiliations and consistent data everywhere they appear. The provider entity work that supports local SEO is the same work that gets a practice into AI answers.
The question set is different here
Patients ask models about symptoms, urgency, insurance and cost before they ever ask for a practice name. Content that answers those upstream questions in quotable, clinician-reviewed passages is what earns the citation.
Everything you get
- Baseline scan of what the major models say about your practice and competitors
- Tracked prompt set built from real patient questions
- Correction work where models state outdated or wrong facts
- Provider entity work: credentials, affiliations, consistent data
- Directory and review platform presence where models actually cite
- Clinician-reviewed content structured for extraction
- Physician and MedicalOrganization structured data
- Quarterly citation share reporting against named competitors
Common questions
Is this safe territory for a medical practice?
The work itself is conservative: correcting facts, completing profiles, marking up real credentials and publishing clinician-reviewed answers to questions patients already ask. Nothing involves making claims. The riskier position is the current default, where a model describes your practice from stale third-party data and nobody at the practice knows what it says.
A chatbot told a patient something wrong about us. What now?
Trace it, then fix the sources. Models repeat what directories, review sites and your own pages say, and outdated entries are usually findable. We correct the originals, update your structured data, and rerun the prompt set over the following weeks to confirm the answer changed. There is no hotline to a model, but source correction genuinely works.
Do patients actually use AI this way?
Increasingly, and measurably in referral patterns for practices we scan. Health questions are among the most common things people ask assistants, and the assistants respond by naming providers. You can see it yourself in one sitting: ask the major models who to see for your specialty in your area, and note who gets named. That is the baseline we start from.
Want a straight read on where you actually stand?
Book a free assessment. We will have looked before the call, and you keep the findings either way.