We extract doctor profiles, clinic details, patient reviews, pricing, and availability from Doctoralia. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Provider Profiles objects from doctoralia.com. All fields typed and schema-versioned.
"provider_id": "dr-ana-silva-982", "full_name": "Dr. Ana Silva", "specialties": "['Cardiology', 'Internal Medicine']", "rating": 4.9, "review_count": 342, "experience_years": 14, "languages_spoken": "['Spanish', 'English']", "registration_number": "MED-849201"
| # | provider_id | url | full_name | specialties | diseases_treated | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Clinic Locations objects from doctoralia.com. All fields typed and schema-versioned.
"clinic_id": "cl-cardio-center-madrid", "provider_id": "dr-ana-silva-982", "clinic_name": "Madrid Cardio Center", "city": "Madrid", "postcode": "28001", "coordinate_lat": 40.4168, "coordinate_lng": -3.7038, "is_premium_profile": true
| # | clinic_id | provider_id | clinic_name | address_line | city | postcode |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Patient Reviews objects from doctoralia.com. All fields typed and schema-versioned.
"review_id": "rev-9482719", "provider_id": "dr-ana-silva-982", "rating": 5, "review_text": "Excellent consultation. Very thorough examination and clear explanations.", "date_posted": "2023-11-14", "verified_visit": true, "condition_treated": "Hypertension", "wait_time_rating": 4
| # | review_id | provider_id | patient_name | rating | review_text | date_posted |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Appointment Slots objects from doctoralia.com. All fields typed and schema-versioned.
"provider_id": "dr-ana-silva-982", "clinic_id": "cl-cardio-center-madrid", "date": "2023-12-01", "time": "14:30", "is_available": true, "consultation_type": "In-person", "price": 120.0, "currency": "EUR"
| # | slot_id | provider_id | clinic_id | date | time | is_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Services objects from doctoralia.com. All fields typed and schema-versioned.
"provider_id": "dr-ana-silva-982", "service_name": "First Cardiology Consultation", "price_min": 120.0, "price_max": 150.0, "currency": "EUR", "is_telemedicine": false, "duration_minutes": 45, "special_instructions": "Please bring previous ECG records."
| # | service_id | provider_id | service_name | price_min | price_max | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Doctoralia scraper handles complex directory structures, dynamic availability calendars, and paginated patient reviews. We bypass anti-bot protections to deliver structured healthcare data.
Extract names, specialties, medical registration numbers, education history, and spoken languages for every listed doctor.
Capture clinic names, exact addresses, geographic coordinates, and facility amenities associated with each provider.
Scrape full review text, star ratings, verified visit badges, and specific condition tags across all paginated review views.
Parse dynamic JavaScript calendars to extract open appointment slots, consultation types, and booking constraints.
Extract minimum and maximum consultation fees, service descriptions, and specific procedure costs.
Map which providers accept specific private health insurance plans, mutuals, and state healthcare coverage.
Extract the specific conditions and diseases a provider treats, allowing for highly granular directory filtering.
Identify providers offering video consultations, remote follow-ups, and digital prescription services.
Scrape data across the entire Docplanner network, including Doctoralia Spain, Brazil, Mexico, and Italy.
Brief in. Clean data out.
Provide target regions, specialties, or specific provider URLs. We map the required schema fields.
We configure Scrapy crawlers, Playwright instances for calendar rendering, and proxy rotation to handle Doctoralia's rate limits.
Automated checks for null rates, coordinate validity, and review pagination completeness before production launch.
Clean JSON or Parquet pushed to your designated S3 bucket or data warehouse on a scheduled cadence.
Doctoralia employs strict rate limiting and dynamic JavaScript rendering to protect its directory. Here is how our infrastructure maintains constant extraction.
Doctoralia actively blocks datacentre IPs and flags anomalous request headers. We route requests through geographically matched residential proxies and normalise TLS fingerprints to mimic standard patient browsing behaviour.
Appointment slots and calendar widgets are not present in the static HTML. We use Playwright to execute page scripts, trigger calendar hydration, and extract real-time availability data directly from the DOM.
Top providers have thousands of reviews spanning hundreds of pages. Our crawlers manage stateful pagination, ensuring complete extraction of the review corpus without triggering rate limits or session timeouts.
The platform frequently tests new profile layouts and premium widget designs. We employ multi-layered CSS and XPath selectors with fallback logic to ensure field extraction succeeds regardless of the active A/B test.
Instead of re-scraping static education history daily, we hash profile states and only extract updated availability slots, new reviews, and pricing changes, drastically reducing pipeline execution time and downstream compute.
Digital health platforms aggregate provider profiles and specialty data to populate their own patient-facing directories.
Private clinic groups monitor local consultation fees and procedure pricing to optimise their own service rates.
Agencies track patient sentiment and review velocity across clinics to manage public relations for healthcare providers.
Insurtech companies analyse which providers accept specific mutuals to identify coverage gaps in regional networks.
Startups monitor the availability of remote consultation slots to build aggregated telemedicine booking interfaces.
Pharmaceutical and medical device companies build targeted outreach lists based on provider specialties and clinic locations.
"Doctoralia holds the most comprehensive map of private healthcare providers, patient sentiment, and consultation pricing available on the public web."
Extracting this data reliably requires navigating strict rate limits, complex JavaScript calendars, and structural variations across the Docplanner network. DataFlirt manages the extraction infrastructure entirely, delivering clean, normalised healthcare data directly to your warehouse so your engineering team can focus on product development.
Everything supported by our doctoralia.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy manages crawl queues and deduplication, while Playwright handles complex interactions like calendar hydration and cookie consent acceptance.
We utilise residential proxy pools matched to the target Doctoralia region (e.g., Spanish IPs for doctoralia.es) to minimise block rates.
Pipelines are scheduled via Apache Airflow and executed on scalable Kubernetes clusters, ensuring data delivery meets strict SLAs.
Data delivered to where your team already works — no new tooling required.
About doctoralia.com scraping, legality, and pipeline operations.
Ask us directly →Yes. The underlying Docplanner architecture is similar across regions. We support extraction from Doctoralia Spain, Brazil, Mexico, Italy (MioDottore), and other regional variants using a unified output schema.
We use headless browsers to interact with the provider's calendar widget, triggering the JavaScript necessary to load open slots for specified date ranges, capturing the time, consultation type, and price.
We only extract publicly visible information, such as the pseudonymised names left on public reviews. We do not extract private medical records, direct messages, or secure booking details.
For static profile data, we recommend weekly or monthly runs. For dynamic data like appointment availability or new reviews, we can configure daily or even intra-day pipelines for specific provider lists.
We distribute requests across large pools of residential proxies and implement intelligent delays between requests. This mimics normal user behaviour and prevents IP bans.
Yes. We parse the embedded map data on clinic profiles to extract precise latitude and longitude coordinates, enabling geographic mapping of healthcare facilities.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full directory export or continuous monitoring of consultation pricing, we handle the infrastructure. Specify your requirements today.
