US Medical Clinics in Wikidata: Coverage Report February 2026

Key stat: Only 2 US medical clinics (with official websites) appear in Wikidata compared with 3,276 US hospitals—methodology and implications below.

How many US medical clinics are represented in Wikidata—the open knowledge graph that AI assistants like ChatGPT and Perplexity use to recommend healthcare providers? This report presents February 2026 coverage statistics, compares clinics to hospitals, and explains why the gap is both a problem and an opportunity for independent and small-to-medium medical practices.

Key findings

• US medical clinics (with official website) in Wikidata: 2
  We count entities that are either instance of clinic (Q9776) or instance of business with a medical specialty (P1995), located in the United States (P17=Q30), with an official website (P856), and excluding hospitals (Q16917).

• US hospitals in Wikidata: 3,276
  For comparison, entities that are instance of hospital (Q16917) and located in the United States.

• Implication: Hospital coverage is orders of magnitude higher than clinic coverage. The vast majority of US medical clinics—including countless SMB and independent practices—are absent from the knowledge graph that powers AI-driven search and recommendations.

Why this report?

Generative Engine Optimization (GEO) and entity-based visibility depend on structured data. When someone asks an AI assistant for a “cardiologist near me” or “family practice in Austin,” systems that consume Wikidata can only surface clinics that exist there with the right properties (location, specialty, contact, website). Without a Wikidata entity, a clinic is effectively invisible to those answers.

This report uses live SPARQL queries against the public Wikidata Query Service to measure coverage. The methodology is transparent and reproducible; we describe it below so you can interpret the numbers and run similar analyses yourself.

If you are closing this gap for a practice or for clients, GEMflush offers plans and pricing, AI visibility for SEO agencies, and a GEO program for medical, legal, and real estate agencies.

Methodology

What we count as a “US medical clinic”

We use a conservative, SMB-oriented definition:

1. Type: The entity is either
   • instance of clinic (Q9776), or
   • instance of business (Q4830453) and has at least one medical specialty (P1995).
2. Country: United States (P17 = Q30).
3. Verifiability: Has an official website (P856). This indicates a real, identifiable practice rather than a placeholder.
4. Exclusion: We exclude entities that are instance of hospital (Q16917), so large hospitals are not mixed into the clinic count.

This definition aligns with how we model medical clinics for knowledge-graph publishing and AI discoverability (see Medical Clinic Visibility in ChatGPT and The Richest SMB Wikidata Entity: Medical Clinic).

What we count as a “US hospital”

For comparison only:

• Type: instance of hospital (Q16917).
• Country: United States (P17 = Q30).

No filter on website or size; the goal is to show how many hospital entities exist versus clinic (and clinic-like) entities.

Data source and timing

• Endpoint: Wikidata SPARQL.
• Report snapshot: February 2026. Counts were obtained by running the queries in this period; small changes may occur as editors add or remove entities.

Our coverage script runs these same queries and writes results to reports/wikidata-medical-clinic-coverage.json for use in future reports and content.

What the numbers mean

The clinic–hospital gap

• 2 US medical clinics (under our definition) have an official website and are not classified as hospitals.
• 3,276 US hospitals are present in Wikidata.

So Wikidata currently reflects hospital-centric coverage: it is far more complete for institutions that are already notable (hospitals) than for smaller, independent, or specialty clinics. That imbalance matters for:

• Patients asking AI for local or specialty care options.
• Practices that want to be recommended by AI but are not yet in the graph.
• Researchers and marketers studying GEO and entity-based visibility.

Why so few clinics?

Possible factors:

• Notability and editing effort: Hospitals often have Wikipedia articles and dedicated editors; clinics usually do not.
• Data sourcing: Bulk and institutional datasets (e.g. government or accreditation) tend to emphasize hospitals.
• Awareness: Many clinic owners and marketers do not yet think of Wikidata as a channel for AI visibility.

The result is a large opportunity: clinics that get a well-structured Wikidata entity with website, location, and specialty can stand out in a still-sparse segment of the graph.

What clinics can do

1. Get your practice into Wikidata with the right type (clinic or business + medical specialty), country, official website (P856), location, and medical specialty (P1995). That is the baseline for being considered by systems that read from Wikidata.
2. Add references (e.g. P973 “described at URL”) from your website, NPI listing, or reputable directories so the entity meets notability expectations and is more likely to be trusted by both humans and algorithms.
3. Enrich the entity with coordinates, contact info, and identifiers (e.g. NPI) where appropriate, so that “near me” and specialty-based queries can match your practice.

For a step-by-step angle, see How to Get Your Medical Clinic in ChatGPT and Medical Clinic Visibility in ChatGPT.

Looking ahead

We plan to repeat this coverage report periodically (e.g. monthly or quarterly) and to extend it with:

• Counts by state when enough clinic entities have location (P131) to make state-level aggregation meaningful.
• New and updated clinic entities over time (e.g. via recent-change or revision data).
• Richness metrics (property count, P973/references) to track not only how many clinics are in Wikidata but how well they are described.

If you publish or improve medical clinic entities on Wikidata, you are directly increasing the denominator in future reports and helping close the clinic–hospital coverage gap.

Summary

US medical clinic coverage in Wikidata remains very low compared with hospitals. Publishing and enriching clinic entities—with official website, location, specialty, and references—is a concrete way to improve AI visibility and to make future coverage reports reflect a more representative picture of US healthcare in the knowledge graph.

Related reading

• Medical Clinic Visibility in ChatGPT — How clinics get discovered by AI assistants
• How to Get Your Medical Clinic in ChatGPT — Step-by-step guide
• The Richest SMB Wikidata Entity: Medical Clinic — What a strong clinic entity looks like
• Generative Engine Optimization for Medical Clinics — GEO strategies for healthcare
• Knowledge Graphs for Medical Clinic Visibility — Why structured data matters

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Data for this report was generated using the Wikidata Query Service. Coverage counts are produced by SPARQL queries that follow the methodology above; the same logic is available in the project’s coverage stats script for reproducibility and future reports.