Contributors:
Amol Ghemud Published: February 19, 2026
Summary
AI platforms cite health content aggregators and government institutions over individual hospitals by a massive margin. The Surfer AI Citation Report, analyzing 36+ million AI Overviews, found that NIH commands roughly 39% of health citations, Healthline captures around 15%, and Mayo Clinic and Cleveland Clinic together account for nearly 29%. Individual hospitals, specialty clinics, and healthtech platforms barely register.
In India, the gap is sharper: Practo, 1mg, and PharmEasy dominate AI responses for provider-related queries while hospitals with 20+ years of clinical expertise remain invisible. The structural advantages aggregators have include comprehensive coverage at scale, structured data implementation, daily update frequency, established domain authority, and alignment with third-party validation. This report presents the data, explains why the gap exists, and outlines what providers must do to close it.
Medical disclaimer: This article presents research on digital marketing and AI citation patterns in healthcare. It does not constitute medical advice, clinical guidance, or treatment recommendations. All healthcare marketing must comply with CDSCO regulations, NABH standards, and applicable medical advertising guidelines.
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Why AI platforms cite health aggregators over individual hospitals by a massive margin, and what providers must do to close the gap
The most comprehensive study of AI citation patterns in healthcare comes from Surfer’s AI Citation Report, which analyzed over 36 million AI Overviews across industries. For health queries specifically, the citation hierarchy is stark.
NIH dominates at roughly 39% of citations. This makes sense. Government health institutions carry the highest institutional trust signals. AI systems treat NIH content as verified, authoritative, and current because of its institutional backing, peer-review process, and consistent publishing standards.
Healthline captures approximately 15% of health AI citations. Mayo Clinic follows at about 14.8%, and Cleveland Clinic at around 13.8%. ScienceDirect rounds out the top five at 11.5%. Notice what’s absent from this list: individual hospitals, specialty clinics, regional health systems, or healthtech platforms.
A separate analysis by Outcomes Rocket examined 5,472 unique citations generated by ChatGPT, Google Gemini, Claude, and Perplexity in August 2025 to determine which health sources the AIs referenced most frequently. The pattern held. Government agencies, major academic medical centers, and commercial health aggregators dominated. Regional providers were virtually absent.
An arXiv research paper on authority signals in AI-cited health sources analyzed 615 ChatGPT health-related citations and found that the top 10 organizations accounted for 325 of 615 total citations, representing 52.8%. The remaining 47.2% was spread across hundreds of sources, with most individual providers appearing once or not at all.
The pattern is consistent across every study: AI cites institutional authority and aggregator scale. Individual clinical expertise, no matter how exceptional, doesn’t register unless it’s structured for AI discovery.
Why aggregators win: the structural advantage AI rewards
Understanding why aggregators dominate AI citations requires understanding what AI systems actually evaluate when selecting sources.
Comprehensive coverage beats deep expertise: Practo covers 50,000+ doctors across dozens of specialties with standardized pages for every condition and treatment. Your hospital might have the best cardiac surgeon in Maharashtra, but if your website has 15 pages on cardiac services while Practo has 500, AI will see Practo as the more comprehensive source. AI systems reward breadth of coverage because it reduces the number of sources they need to cite per query.
Structured data at scale: Aggregators invest heavily in schema markup, structured data, and machine-readable content formats. Every doctor listed on Practo includes structured physician data, specialty classifications, location data, availability, and patient reviews, all in formats that AI can parse instantly. Most hospital websites store the same information in unstructured paragraphs, PDFs, or image-based content that AI systems struggle to extract.
Update frequency signals freshness: Practo updates thousands of listings daily as doctors change availability, patients leave reviews, and pricing adjusts. This constant update frequency signals to AI systems that it is fresh. Your hospital website might update clinical content quarterly, if that’s the case. AI platforms weigh medical content freshness as a safety signal.
Domain authority compounds over time: Aggregators have spent years building backlink profiles, publishing content at scale, and earning domain authority through volume. For providers without national recognition, the domain authority gap compounds the citation disadvantage.
Third-party validation alignment: Hospitals that maintain alignment across Healthgrades, U.S. News rankings, NIH-linked resources, and insurance directories are cited more often because AI systems can cross-reference and verify claims with greater confidence. Aggregators inherently have this validation because they ARE the third-party directories AI references.
The Indian healthcare market: Practo, 1mg, and the provider citation desert
The global dominance of aggregators is even more pronounced in India’s healthcare market.
Run this experiment yourself. Ask ChatGPT: “Best orthopedic hospital in Pune.” Ask Perplexity: “Top cardiac surgeon in Mumbai.” Ask Google’s AI Overview: “IVF treatment cost in Bangalore.” Count how many times the response cites an actual hospital versus Practo, 1mg, or PharmEasy.
In our monitoring across 50 healthcare queries targeting Indian providers, aggregators appeared in 70-85% of AI responses while individual hospitals appeared in under 15%. The remaining citations were to international sources such as Mayo Clinic, Healthline, and government health portals.
This creates a specific problem for Indian healthcare providers. The aggregator isn’t just getting cited more. It’s defining how AI describes your specialty. When Practo’s generic description of “knee replacement surgery” gets cited instead of your orthopedic department’s specific approach, the AI response strips away everything that differentiates your clinical practice.
Three factors make the Indian aggregator gap particularly severe.
First, Indian hospital websites are, on average, less technically optimized than their Western counterparts. Many still use image-heavy designs with clinical information locked in PDFs or Flash-based elements. The content that exists is often promotional rather than clinical, which AI platforms penalize under YMYL evaluation.
Second, India’s healthcare aggregators have invested specifically in AI readiness. Practo’s API integrations, structured data implementation, and content standardization were built for programmatic access. 1mg’s drug information database is structured for machine reading. These platforms were designed for the digital ecosystem. Most hospital websites were designed for human visitors browsing on desktop computers.
Third, the physician credentialing gap is massive. On Practo, every doctor has a standardized profile that includes registration numbers, qualifications, experience, and patient reviews. On most hospital websites, doctor profiles are unstructured marketing pages without schema markup, publication links, or machine-verifiable credentials.
When upGrowth helped Digbi Health achieve 500% organic traffic growth, one of the first discoveries was that aggregator content was appearing in AI responses for queries Digbi’s platform was clinically better positioned to answer. The fix wasn’t creating more content. It involved restructuring existing clinical content to meet the E-E-A-T signals that AI systems require.
What AI citation winners do differently: the provider playbook
The hospitals and health systems that DO earn AI citations share specific patterns.
Structured clinical detail on every specialty page. Cleveland Clinic’s condition pages include symptoms, causes, diagnostic methods, treatment options, recovery timelines, and guidance on when to seek care, all in structured formats with clear headings that AI can extract as passage-level citations. Compare that to the average Indian hospital specialty page: a paragraph of marketing copy, a stock photo, and a “book appointment” button.
Physician pages that AI can verify. Mayo Clinic’s physician profiles include publication lists linked to PubMed, board certifications with verification paths, specialty training details, and research interests. This gives AI systems multiple verification paths to confirm expertise. A Practo profile has some of these elements. Most hospital websites have none.
Content that answers the exact query patients type into AI. BrightEdge data shows that 89% of healthcare queries now trigger AI Overviews. The providers cited in those Overviews include content that directly answers the specific question in the first paragraph and provides supporting clinical detail. They don’t bury the answer under institutional history and marketing language.
Multi-source validation infrastructure. Cited providers maintain consistent information across their website, Healthgrades, Google Business Profile, Wikipedia, and medical directories. When AI systems cross-reference a claim across multiple sources and find consistency, citation confidence increases.
YMYL compliance as a competitive weapon. The providers earning AI citations treat YMYL compliance not as a box to check but as a structural advantage. Named medical authors with verifiable credentials, primary source citations with dates, medical disclaimers, and regularly updated clinical data. These are the exact signals AI platforms evaluate when deciding whether to cite a health source or skip it.
The closing window: why provider citation share must be built now
The aggregator citation advantage compounds monthly. Every month Practo earns citations that your hospital doesn’t, and two things happen. First, AI systems build stronger trust in Practo for your specialty queries. Second, your absence from AI responses becomes the default that new AI model training reinforces.
This isn’t theoretical. ChatGPT Health launched in January 2026, and over 40 million people globally now query ChatGPT daily for health information, according to OpenAI. Capgemini Research Institute found that 58% of consumers used generative AI for product and service recommendations in 2025, up from 25% in 2023.
Patient behavior has already shifted. The question isn’t whether patients will use AI to make healthcare decisions. They already are. The question is whether AI will cite your clinical expertise or Practo’s generic listing when those patients ask.
Seer Interactive’s September 2025 data shows that organic click-through rates drop to 0.6% when Google provides an AI Overview, compared to 1.6% without one. With BrightEdge confirming that 89% of healthcare queries trigger AI Overviews, the traditional website traffic model is structurally broken for healthcare providers who don’t appear in AI-generated answers.
The providers who build citation authority now will own the AI response layer for their specialties. Those who wait will need to overcome entrenched citation patterns that get more expensive to displace every quarter.
The provider vs aggregator citation gap is the defining challenge
Clinical expertise that took decades to build becomes invisible when AI systems can’t verify, extract, or cite it. The data in this report shows both the scale of the problem and the specific structural fixes that close the gap.
The hospitals that act on this data in 2026 will own the AI citation layer for their specialties. Those who wait will find that gap more expensive to close every quarter.
upGrowth works with hospitals and healthtech companies to systematically close the provider vs. aggregator citation gap. Our healthcare marketing services start with an AI citation audit that measures exactly where your hospital appears versus aggregators across your core specialties, then build the structured data, physician verification, and YMYL compliance infrastructure that earns AI citations. If you want to understand your current citation baseline and what it takes to compete with aggregators for your specialty queries, the first step is measuring the gap.
1. Which healthcare sources do AI platforms cite most frequently?
Government institutions (NIH at approximately 39%), commercial health aggregators (Healthline at around 15%, WebMD), and major academic medical centers (Mayo Clinic at roughly 14.8%, Cleveland Clinic at 13.8%) dominate AI health citations. Individual hospitals and regional health systems appear in fewer than 5% of AI responses to most healthcare queries, according to the Surfer AI Citation Report, which analyzes 36+ million AI Overviews.
2. Why does Practo get cited by AI instead of my hospital?
Practo has three structural advantages: comprehensive coverage of thousands of conditions and doctors, with standardized pages; structured data implementation that AI systems can parse instantly; and daily updates that signal content freshness. Most hospital websites lack structured data, update infrequently, and store clinical information in formats AI can’t easily extract.
3. Can a single hospital realistically compete with aggregators for AI citations?
Not across all queries, but absolutely for their specialty areas. Cleveland Clinic doesn’t try to compete with Practo on general doctor listings. It dominates AI citations in cardiology, neurology, and related specialties. Individual hospitals should focus GEO efforts on 5-10 core specialties where they have genuine clinical authority, rather than trying to win citation share across all healthcare queries.
4. How long does it take to close the provider vs aggregator citation gap?
Technical implementation (schema markup, physician profiles, structured content) can produce initial citation improvements within 4-8 weeks for Perplexity and Google AI Overviews. Building sustained citation authority that competes with aggregators for core specialty queries typically takes 6-12 months of consistent GEO effort. The gap closes faster for niche specialties with less aggregator competition.
5. What’s the first thing a hospital should do to improve AI citation share?
Run an AI citation audit. Query your top 10 specialties across ChatGPT, Perplexity, and Google AI Overviews. Document who gets cited (you, aggregators, competitors, or nobody). This baseline reveals exactly where the gaps are and which specialties represent the highest-value citation opportunities.
For Curious Minds
Institutional authority is the trust signal an AI assigns to a source based on its perceived credibility, scale, and official status, not just its clinical expertise. AI systems favor large, recognized entities because their content is presumed to be vetted, regularly updated, and comprehensive. For example, an organization like the NIH, which secures nearly 39% of AI health citations, wins because of its governmental backing and massive, structured content library. Your hospital must work to build similar signals by investing in a robust, data-rich digital presence. This authority is built through:
Consistent publication of peer-reviewed or expert-validated content.
Strong backlink profiles from other authoritative domains.
High-frequency updates that signal content freshness and reliability.
Discovering the full framework for building this authority is critical for gaining visibility.
The "structural advantage" refers to how health aggregators organize and present information at a scale and format that AI systems are built to reward. This advantage is not about clinical superiority but about data accessibility, leading to a significant citation gap. Aggregators like Practo dominate because they master the core elements AI values. Providers must adopt these structural principles to compete effectively. Key components include:
Comprehensive Coverage: Offering thousands of standardized pages on conditions and treatments.
Structured Data: Using schema markup that AI can parse instantly.
Update Frequency: Constant updates signal content is fresh and reliable.
Domain Authority: Built over years with massive content volume.
Understanding how to implement these structural elements is the first step to closing the gap.
Third-party validation alignment is the process by which an AI system verifies a provider's claims by cross-referencing them with other trusted sources. It's a critical factor because AI prioritizes sources it can independently confirm, effectively reducing its risk of providing inaccurate information. A hospital's website is never judged in isolation; it's evaluated against its entire digital ecosystem. This is why a consistent presence is crucial. Strong alignment includes:
Matching physician credentials on your site with listings on Healthgrades.
Ensuring services listed align with rankings on platforms like U.S. News.
Linking to supporting research from authoritative domains like the NIH.
When these sources align, it builds a powerful, unified signal of trust that AI systems are designed to reward.
The difference comes down to how information is packaged for a machine, not a human. A hospital may have the best cardiac surgeon, but an aggregator like Healthline, which captures around 15% of AI citations, presents its broader cardiac content in a way that AI prefers. The key is translating deep expertise into a machine-readable format. Aggregators win because they consistently implement:
Schema Markup: Explicitly tagging physician names, specialties, and locations.
Content Breadth: Having 500 pages on a topic versus a hospital's 15.
Interlinking: Creating a dense web of related content that signals topical authority.
Consistent Updates: Signaling freshness, which AI interprets as a safety and relevance factor.
Replicating these data structure tactics is essential for any provider seeking AI visibility.
This stark statistic reveals that AI systems have a strong concentration bias, rewarding a very small number of sources that exhibit clear signals of high authority. With over 52.8% of citations going to just 10 entities, individual providers are rendered invisible because they lack the scale and data structure AI uses as a proxy for trust. The AI isn't evaluating clinical quality; it's pattern-matching for authority signals that only large players like Mayo Clinic currently provide. The core issue is that AIs equate comprehensiveness and structured data with credibility. Without a strategy to build these digital assets, most providers will remain outside this circle of trust. To learn how to build these signals, you must analyze what top performers do differently.
Institutions like Mayo Clinic, which garners about 14.8% of health AI citations, succeed by acting like publishers, not just clinical providers. They combine deep expertise with the structural advantages of an aggregator, creating a powerful signal for AI systems. Regional hospitals can adopt a "mini-aggregator" model for their own specialties. Replicable strategies include:
Creating comprehensive, A-to-Z guides for core service lines, structured with clear headings and internal links.
Implementing detailed schema for every physician, procedure, and location.
Establishing a regular content update cadence, even for minor informational changes, to signal freshness.
Building a library of patient-facing articles that answer common questions.
Adopting these publisher-centric tactics is the most direct path to improving AI visibility.
To close the AI citation gap, a regional health system must shift from creating sporadic content to building a structured information hub. The goal is to make your deep expertise machine-readable and appear more comprehensive to AI crawlers. Start by focusing on your most valuable service line and treating it like a standalone publication. The first three steps are:
Conduct a Content Audit and Structure Overhaul: Consolidate all related content into a single, well-organized section. Use structured data (schema markup) for every physician, service, and location mentioned.
Develop Pillar Content: Create extensive pillar pages for your top procedures, covering symptoms, treatments, and FAQs. This mimics the comprehensive coverage AI rewards in aggregators like Practo.
Establish an Update Cadence: Schedule monthly or quarterly reviews of your key pages to update statistics or simply note a review date. This signals freshness.
These foundational steps begin building the authority signals AI systems are trained to look for.
Unstructured data like paragraphs and PDFs forces an AI to guess at information, which it avoids doing for high-stakes health queries. AI systems prioritize verifiable, clearly labeled data, which is why aggregators that use structured formats are cited more often. Implementing schema markup is the solution to translate your content into a language AI understands. For physician information, you should implement:
Physician Schema (`Physician`): This includes fields for `name`, `medicalSpecialty`, `alumniOf`, and `knowsAbout`.
Hospital Schema (`Hospital`): This provides context, linking the physician to their affiliated institution, including `address` and `telephone`.
Review Schema (`Review`): Aggregating patient testimonials in a structured format provides social proof that AIs like Google Gemini can cross-reference.
This small technical change makes a massive difference in how AI perceives and trusts your content.
The long-term implication is a significant decline in organic patient acquisition, as AI-driven answers will intercept users before they ever reach a hospital's website. If providers are not cited, they will become invisible to a growing segment of prospective patients who trust AI-generated summaries. Digital strategy must evolve from traditional SEO to "AI optimization", focusing on becoming a primary source. This evolution requires:
Shifting budget from broad advertising to building a deep, structured content library.
Prioritizing technical SEO, especially schema markup, to make content machine-readable.
Focusing on third-party validation by aligning content across platforms like Healthgrades and U.S. News.
Without this strategic shift, regional hospitals risk being completely bypassed in the patient journey.
Providers must shift their view of online reputation from a marketing task to a core data integrity function. AI systems build confidence by cross-referencing claims across multiple authoritative domains, so inconsistencies in data act as a major red flag that reduces citation potential. Your strategy must be to create a "web of trust" around your brand. This involves meticulously managing your digital footprint across all platforms, including:
Ensuring physician names and specialties are identical on your website, Healthgrades, and insurance directories.
Actively linking to and from high-authority sources like NIH studies to validate your content.
Aligning the services you highlight with those recognized in your U.S. News rankings.
This alignment creates a verifiable identity that AI systems are more likely to trust and cite.
The fundamental mismatch is that clinics present expertise for human interpretation, while AIs need data for machine verification. A world-class surgeon's reputation is built on outcomes, but an AI sees only unstructured text and a short bio. To resolve this, clinics must translate human expertise into digital authority signals. This requires a strategic solution:
Quantify Expertise with Content: Instead of just stating expertise, demonstrate it with comprehensive guides on conditions and treatments.
Structure Everything: Use schema markup to explicitly define the expert's specialty, credentials, and published works.
Build Digital Connections: Actively seek backlinks from academic institutions, medical societies, and reputable health publishers like Healthline.
This approach makes expertise discoverable and verifiable, bridging the gap between real-world authority and AI-perceived credibility.
The most common mistake is creating content that is too narrow and internally focused, such as press releases, while neglecting to build a comprehensive, patient-facing knowledge base. AI systems ignore this content because it lacks the breadth needed to answer user queries. To pivot, hospitals must adopt a public service mindset for their content strategy, similar to the NIH. This involves:
Answering broad health questions, not just promoting specific services.
Organizing information into clear, interconnected topic clusters.
Committing to regular updates to ensure accuracy and freshness, a key factor that helps the NIH secure 39% of citations.
This strategic shift from self-promotion to public education is how providers can begin to build true digital authority.
Amol has helped catalyse business growth with his strategic & data-driven methodologies. With a decade of experience in the field of marketing, he has donned multiple hats, from channel optimization, data analytics and creative brand positioning to growth engineering and sales.
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