6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Direct Patient Answer' First-Paragraph Rule
Patient Query Extraction Score
Implementation Pattern
"Provide a concise, declarative answer to the patient's implicit health query in the first 40-60 words of the article."
Citation Triggers
AI models are trained to extract information from the most context-dense parts of a page. Answer the 'What/Why/How' of a condition or treatment immediately, keep the primary definition sentence bolded, and use objective medical language ('[Condition] is a disorder characterized by...').
02
Structural SpecSemantic Condition Hierarchies (H2 & H3)
Topical Medical Coverage
Implementation Pattern
"Use headers as logical nodes in a medical knowledge graph rather than just for visual formatting."
Citation Triggers
Every H2 should identify a core medical concept or condition, and every H3 should provide a supporting symptom, treatment, or diagnostic detail. Ensure headers match 'People Also Ask' (PAA) intent regarding patient concerns and avoid jargon where possible; use descriptive medical terms that LLMs can tokenize easily.
03
Metadata SpecJSON-LD: The Patient-Readable Layer
Crawl Reliability for Health Data
Implementation Pattern
"Deploy MedicalWebPage, MedicalCondition, Physician, and FAQPage schemas as the primary data exchange layer."
Citation Triggers
Structured data tells the engine explicitly what it is looking at. FAQPage schema enables direct extraction of patient Q&A pairs, while MedicalWebPage and MedicalCondition schemas drive accurate indexing for health-related searches and direct answers in generative AI interfaces.
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04
Context SpecEntity-Based Medical Neighborhoods
Medical Entity Association
Implementation Pattern
"Map your content to related medical entities expected by LLMs for a given health topic."
Citation Triggers
LLMs don't just match keywords; they map entities. If you target 'Type 2 Diabetes', the model expects to find related entities like 'Insulin Resistance', 'Blood Glucose Levels', 'HbA1c', and 'Metformin'. Maintain consistent co-occurrence of these terms to build clinical authority.
05
Authority SpecClinical Uniqueness & Evidence-Based Citations
Citation Probability for Health Content
Implementation Pattern
"Share unique patient outcome data, proprietary treatment protocols, and physician insights to gain 'Primary Source' status."
Citation Triggers
Generative AI models prioritize unique, authoritative data. Publishing annual 'Clinic Performance' reports or case studies gives your content a high uniqueness score. Being cited by authoritative medical journals or health directories is the strongest signal in the AEO ecosystem for clinics.
06
Formatting SpecSymptom-to-Statement Mapping
LLM Ingestion Quality for Medical Data
Implementation Pattern
"Format lists of symptoms or treatment steps as declarative 'Clinical Statements' rather than marketing bullet points."
Citation Triggers
LLMs excel at parsing structured lists using <ul> and <li> tags. Instead of 'We offer fast relief,' use '[Condition] is often managed with [Treatment X], which has shown to reduce symptom severity by [Y]%' to facilitate accurate machine extraction for patient understanding.
Pro Tips & Insights
01
AEO is the 'Zero-Click' strategy for patient acquisition. Even if patients don't visit your site, seeing your clinic cited as the authoritative answer builds massive long-term Trust and Recall for health services.
02
Formatting is a ranking factor for health queries. Machine-readability is the prerequisite for visibility. If an AI model can't parse your clinical data structure, it won't cite you for patient information.
03
The 'Brand Moat' in AEO for clinics is 'Co-occurrence'. You want search models to associate '[Your Clinic Name]' with phrases like 'The most trusted [Specialty] clinic in [Location]'.
04
Declarative Health Truths win. AI models favor objective, verifiable medical facts over subjective marketing claims. Shift your copy from 'Marketing' to 'Evidence-Based Education'.
