🏥 CNES Brazil Health Establishments Scraper

🚀 Export Brazil's CNES national registry of health establishments in seconds. Hospitals, clinics, managers, services, and bed counts.

🕒 Last updated: 2026-05-29 · 📊 11 fields per record · All 27 Brazilian states · 350,000+ establishments

The CNES Brazil Health Establishments Scraper turns the cnes.datasus.gov.br public registry into a structured dataset. It calls DataSUS's establishment lookup endpoint and returns one row per facility, including CNES code, official name, type, manager, address, services, and bed counts.

Coverage spans the entire Cadastro Nacional de Estabelecimentos de Saúde maintained by Brazil's Ministry of Health, with filters by state and municipality.

🎯 Target Audience	💡 Primary Use Cases
🏥 Health system analysts	Map facility distribution by region
📊 Public health researchers	Quantify hospital bed capacity per municipality
🏛️ Policy makers	Audit primary care coverage
📰 Journalists	Verify establishment ownership and management
🤖 Data engineers	Mirror CNES into your own warehouse
🎓 Academics	Build study cohorts of Brazilian health facilities

📋 What the CNES Brazil Health Establishments Scraper does

• Queries the public CNES services endpoint with optional state, city, and name filters.
• Flattens each establishment record into a clean row with normalized field names.
• Combines street, number, and neighborhood into a single address string.
• Casts bed counts to numbers for clean BI ingestion.
• Surfaces upstream errors as a single diagnostic row instead of crashing.

💡 Why it matters: CNES is the authoritative source on Brazil's 350,000+ health establishments, but its public viewer is a Java applet style page that's painful to scrape. This actor exposes the raw JSON directly.

🎬 Full Demo

🚧 Coming soon.

⚙️ Input

Field	Type	Required	Description
state	enum	No	Two-letter state code or empty for all states.
city	string	No	6-digit IBGE municipal code.
search	string	No	Filter by establishment name.
maxItems	integer	No	Free 10, paid up to 1,000,000.

Example 1 - All São Paulo state hospitals:

{ "state": "SP", "maxItems": 100 }

Example 2 - Search by name in Rio de Janeiro municipality:

{ "state": "RJ", "city": "330455", "search": "HOSPITAL", "maxItems": 50 }

⚠️ Good to Know: CNES updates daily. Bed counts may differ from facility websites if the facility has not updated its CNES registration recently.

📊 Output

Field	Type	Description
🆔 cnes_id	string	CNES national identifier.
🏥 name	string	Establishment name.
🩺 type	string	Facility type.
👤 manager	string	Management entity.
📍 address	string	Combined street, number, and neighborhood.
🏙️ city	string	Municipality name.
🗺️ state	string	State UF code.
💼 services	string	Reported services.
🛏️ beds	number	Total bed count.
🕒 scrapedAt	string	When this row was fetched.
❌ error	string	Set if the upstream response was an error.

Sample record:

{
  "cnes_id": "2077485",
  "name": "HOSPITAL DAS CLINICAS DA FMUSP",
  "type": "HOSPITAL GERAL",
  "manager": "MUNICIPAL",
  "address": "AV DR ENEAS DE CARVALHO AGUIAR, 255, CERQUEIRA CESAR",
  "city": "SAO PAULO",
  "state": "SP",
  "services": null,
  "beds": 2200,
  "scrapedAt": "2026-05-29T13:00:00.000Z",
  "error": null
}

✨ Why choose this Actor

| 🆓 | Works with no API key. | | 🇧🇷 | All 27 states, every municipality. | | 🔢 | Bed counts cast to real numbers. | | 🧹 | Normalized field names ready for BI. | | 💾 | Push to dataset and export CSV, Excel, JSON, or XML. |

📈 How it compares to alternatives

Approach	Setup time	Clean rows	Coverage	Maintained
Manual CNES web UI	10 min per filter	❌	partial	manual
DataSUS bulk dumps	hours to parse	partial	full	quarterly
This Actor	5 sec, no install	✅	full	live

🚀 How to use

1. Click Try for free.
2. Pick a state and optionally a city code or name search.
3. Click Start. Your dataset is ready within seconds.

💼 Business use cases

🏥 Capacity planning. Map bed counts per state to spot care deserts.

📊 M&A research. Identify hospital networks within a target region.

📰 Newsroom verification. Confirm a facility's official registration and management.

🤖 Geocoding pipelines. Feed the address field into a geocoder to build a map layer.

🔌 Automating CNES Brazil Health Establishments Scraper

• Make / Zapier: trigger and push to Airtable or Google Sheets.
• Cron schedule: native Apify scheduler for nightly refreshes.
• Webhooks: POST to your endpoint after each run.
• Pipe to BigQuery / Snowflake / Postgres: native integrations.

🌟 Beyond business use cases

🎓 Education. Teach health informatics with real Brazilian SUS data.

🧪 Personal research. Map healthcare access in your home state.

🤝 Non-profit. Power patient navigation and referral apps.

🧰 Prototyping. Spin up a dashboard in minutes with real data.

🤖 Ask an AI assistant about this scraper

Paste this README into ChatGPT or Claude and ask it to map your workflow to the actor's inputs.

❓ Frequently Asked Questions

❓ Do I need an API key? No.

❓ Which establishments are covered? Every facility registered with Brazil's CNES.

❓ How fresh is the data? CNES updates daily; this actor reads it live.

❓ Are bed counts cast to numbers? Yes.

❓ Can I filter by city? Yes, pass the 6-digit IBGE code.

❓ Can I schedule runs? Yes via the Apify scheduler.

❓ Is this scraping or API? Public DataSUS JSON services.

❓ What format can I download? CSV, Excel, JSON, JSONL, XML, RSS, or HTML.

❓ What if the search returns no results? A diagnostic record with error is pushed.

❓ Will the schema change? Stable.

🔌 Integrate with any app

Apify ships native integrations with Make, Zapier, Slack, Discord, Google Drive, Google Sheets, Gmail, Airbyte, Keboola, Telegram, GitHub, and any REST API or webhook.

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