We extract doctor profiles, patient reviews, speciality rankings, and facility affiliations from RateMDs. 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 Doctor Profiles objects from ratemds.com. All fields typed and schema-versioned.
"doctor_id": "dr-john-smith-new-york-ny", "first_name": "John", "last_name": "Smith", "speciality": "Cardiologist", "overall_rating": 4.8, "review_count": 342, "accepting_new_patients": true, "claimed_profile": true
| # | doctor_id | first_name | last_name | speciality | gender | accepting_new_patients |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Patient Reviews objects from ratemds.com. All fields typed and schema-versioned.
"review_id": "rvw-98237492", "doctor_id": "dr-john-smith-new-york-ny", "overall_rating": 5.0, "staff_rating": 5.0, "punctuality_rating": 4.0, "helpfulness_rating": 5.0, "knowledge_rating": 5.0, "submission_date": "2026-03-14"
| # | review_id | doctor_id | submission_date | overall_rating | staff_rating | punctuality_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Facilities objects from ratemds.com. All fields typed and schema-versioned.
"facility_id": "fac-mount-sinai-ny", "facility_name": "Mount Sinai Hospital", "facility_type": "Hospital", "city": "New York", "state": "NY", "zip_code": "10029", "affiliated_doctors_count": 1428
| # | facility_id | facility_name | facility_type | address_line_1 | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Speciality Rankings objects from ratemds.com. All fields typed and schema-versioned.
"doctor_id": "dr-john-smith-new-york-ny", "speciality": "Cardiologist", "city": "New York", "state": "NY", "city_rank": 12, "state_rank": 45, "national_rank": 312, "rating_score": 4.8
| # | doctor_id | speciality | city | state | national_rank | state_rank |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from ratemds.com. All fields typed and schema-versioned.
"search_keyword": "Cardiologist", "search_location": "New York, NY", "result_position": 3, "doctor_id": "dr-john-smith-new-york-ny", "is_sponsored": false, "overall_rating": 4.8, "review_count": 342, "distance_miles": 2.4
| # | search_keyword | search_location | result_position | doctor_id | doctor_name | speciality |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our RateMDs pipeline navigates location-based directories, extracts granular sub-ratings, and maps complex provider-to-facility relationships while bypassing strict bot protection.
Extract name, speciality, gender, accepting patients status, and claimed profile flags across millions of provider pages.
Capture overall scores alongside specific ratings for staff, punctuality, helpfulness, and knowledge.
Extract full patient commentary, submission dates, and language flags across paginated review histories.
Map doctors to hospitals and private practices, including facility addresses, phone numbers, and aggregate ratings.
Track provider rankings at the city, state, and national levels for specific medical specialities.
Simulate local searches to capture distance metrics, organic rankings, and sponsored provider placements.
Bypass Cloudflare and IP rate limits using residential proxies and realistic browser fingerprints.
Maintain a hash index of last-seen values to emit only new reviews or rating changes, reducing downstream load.
Configure continuous pipelines at daily or weekly cadences to monitor provider reputation over time.
Brief in. Clean data out.
Provide target specialities, geographic regions, or specific doctor IDs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and CAPTCHA handling for ratemds.com.
Schema validation, null-rate checks, and sample review extraction before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
RateMDs protects its directory with strict rate limits and location-based rendering. Here is how we maintain reliable extraction.
RateMDs relies on Cloudflare and IP reputation scoring. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain access without triggering blocks.
Search results and rankings are highly dependent on user location. We inject specific geographic coordinates and postal codes into the session to extract accurate city and state-level provider rankings.
High-profile doctors have thousands of reviews spanning multiple pages. We handle complex pagination states and asynchronous loading to ensure no historical reviews are missed during full directory scrapes.
Provider pages frequently lack standard formatting for addresses and affiliations. We use fallback selector chains and regex parsing to normalise unstructured text into clean, typed database columns.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing sub-ratings, and coverage drops, responding before the data reaches your warehouse.
Health insurance companies verify provider directory accuracy, checking active status and facility affiliations.
Analysts track speciality distribution and patient demand across specific geographic regions to identify underserved markets.
Hospital networks monitor patient sentiment and staff ratings across their affiliated doctors to improve care quality.
Machine learning teams use the vast corpus of patient reviews to train medical sentiment analysis models and NLP classifiers.
Speciality clinics identify top-rated primary care physicians in their area to build targeted referral partnerships.
Researchers correlate punctuality and staff ratings with overall patient outcomes and satisfaction metrics.
"RateMDs holds the most granular patient sentiment data available, but extracting it requires bypassing strict bot protection and normalising highly unstructured location data."
Healthcare data extraction demands precision. We manage the residential proxies, CAPTCHA solvers, and location-spoofing required to map provider networks and scrape patient reviews at scale. You get clean, compliant records delivered directly to your data warehouse.
Everything supported by our ratemds.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 handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US and CA regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About ratemds.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from RateMDs is generally permissible under applicable law. DataFlirt targets only public, non-authenticated provider profiles, facility listings, and patient reviews. We do not extract personal patient data beyond public usernames, nor do we circumvent authentication walls. Clients should review RateMDs ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions, and request timing modelled on human behaviour. We monitor for rate limits and CAPTCHA challenges in real time, triggering solver queues automatically.
We can extract provider data globally, with extensive coverage across the United States, Canada, and the United Kingdom. We simulate local searches to capture accurate city and state-level rankings.
Full directory refreshes typically run weekly or monthly depending on scope. For specific provider lists, we can configure daily pipelines to monitor new reviews and rating changes with sub-24-hour latency.
Yes. Our crawlers navigate all pagination layers to extract the complete review history for any given provider profile, regardless of review volume.
Yes. We provide a sample run of up to 500 provider profiles or specific speciality searches as part of the pre-engagement scoping process. This allows you to validate schema fit and data quality before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a national provider directory or continuous sentiment monitoring across specific specialities, we scope, build, and operate the pipeline. Tell us what you need.
