Healthcare AEO is a high-leverage channel because patients and caregivers increasingly ask ChatGPT, Claude and Perplexity for health research before Google. LLMs cite healthcare content based on author credibility, factual tightness, schema coverage - doctor-authored content wins. AEO for healthcare engineers condition/symptom/procedure content for LLM extraction. The behaviour driving it is worth understanding precisely. People ask these systems the questions they are embarrassed to ask a doctor and the ones they want answered before booking, which means the queries skew toward symptoms, what to expect and whether something is serious. Models handle general clinical explanation conservatively and reach for recognised sources, so the openings for a hospital sit in the provider layer: which specialists treat this, where, and what the process looks like locally.
Healthcare LLM queries: symptom ('symptoms of X'), condition ('what causes Y'), treatment ('how is Z treated'), specialist-search ('best doctor for X'), procedure ('what to expect during X'). LLMs weight: doctor-authored + MedicalEntity schema + factual tightness + recency. Perplexity and Brave AI fetch live - 2-4 weeks to citation. ChatGPT + Claude 4-8 weeks. Healthcare is also the vertical where these systems are most cautious by design, adding safety framing, declining to give individual advice and preferring established institutional sources, which lowers the ceiling on general clinical queries and raises the value of everything local and provider-specific. A hospital that publishes complete, current information about its departments, its consultants and their qualifications, its locations and its admission process gives a model something concrete to cite when someone asks where to get a condition treated in their city. Consistency across the site, the Google Business Profile and directory listings matters as much as the content itself, since contradictory details reduce the confidence a model places in any of them.
