We extract physician profiles, patient reviews, clinic locations, accepted insurance, and credential data from Vitals. 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 vitals.com. All fields typed and schema-versioned.
"doctor_id": "dr_john_smith_123", "full_name": "Dr. John A. Smith, MD", "specialties": "['Cardiology', 'Internal Medicine']", "gender": "Male", "years_experience": 14, "overall_rating": 4.2, "total_reviews": 87
| # | doctor_id | full_name | first_name | last_name | specialties | gender |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Patient Reviews objects from vitals.com. All fields typed and schema-versioned.
"review_id": "rev_98412", "doctor_id": "dr_john_smith_123", "review_date": "2026-03-14", "overall_rating": 5, "promptness_rating": 4, "bedside_manner_rating": 5, "review_body": "Excellent cardiologist. Took the time to explain the procedure."
| # | review_id | doctor_id | author_name | review_date | overall_rating | promptness_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Locations & Contact objects from vitals.com. All fields typed and schema-versioned.
"practice_name": "Heart Care Associates", "address_line_1": "123 Medical Blvd", "city": "Boston", "state": "MA", "zip_code": "02115", "phone_number": "+1-617-555-0198", "accepting_new_patients": true
| # | location_id | doctor_id | practice_name | address_line_1 | address_line_2 | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Insurance & Networks objects from vitals.com. All fields typed and schema-versioned.
"carrier_name": "Blue Cross Blue Shield", "plan_name": "BCBS Blue Card PPO", "plan_type": "PPO", "medicare_accepted": true, "medicaid_accepted": false, "verification_date": "2026-01-10"
| # | doctor_id | carrier_name | plan_name | network_tier | plan_type | medicare_accepted |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Credentials & Affiliations objects from vitals.com. All fields typed and schema-versioned.
"medical_school": "Harvard Medical School", "graduation_year": 2012, "residency_hospital": "Massachusetts General Hospital", "board_certifications": "['American Board of Internal Medicine']", "hospital_affiliations": "["Brigham and Women's Hospital", 'MGH']", "languages_spoken": "['English', 'Spanish']"
| # | doctor_id | medical_school | graduation_year | residency_hospital | fellowship | board_certifications |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Vitals scraper extracts every layer of physician data: demographic profiles, granular patient feedback, clinical credentials, and complex insurance network matrices - handling rate limits and directory pagination automatically.
Extract full name, gender, specialties, years in practice, and NPI identifiers where surfaced.
Capture overall ratings alongside specific sub-scores: promptness, bedside manner, staff courtesy, and diagnostic accuracy.
Scrape primary and secondary clinic addresses, phone numbers, fax lines, and new-patient acceptance status.
Extract medical school history, residency programs, fellowships, and active board certifications.
Map doctors to their admitting hospitals and clinical network associations across regions.
Capture accepted carriers and specific plan types (PPO, HMO, Medicare, Medicaid) per provider.
Extract reported average wait times and correlate them against patient satisfaction scores.
Scrape spoken languages and recognised industry awards (e.g., Compassionate Doctor Recognition).
Monitor directories for new providers, retired profiles, address changes, or shifting insurance acceptance.
Brief in. Clean data out.
Provide target specialties, geographic regions, or specific provider names. We configure the extraction schema.
We deploy Scrapy/Playwright clusters, configure residential proxies, and handle Vitals' directory pagination.
Schema validation, null-rate checks on critical fields like NPI, and address normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Healthcare directories deploy strict rate limits to prevent bulk data extraction. Here is how we maintain steady throughput without triggering blocks.
Vitals monitors request velocity per IP. We distribute requests across thousands of US residential IPs, maintaining low per-node concurrency to blend with normal patient traffic patterns.
State-level and specialty-level directories often rely on dynamic loading. We use Playwright to execute JavaScript pagination, ensuring we capture providers buried deep in the search results.
Clinic addresses on Vitals are notoriously inconsistent. Our pipeline includes post-processing steps to parse and normalise street addresses, cities, and ZIP codes into structured components.
Insurance data is often presented in complex nested UI elements. We extract both the parent carrier (e.g., Aetna) and the specific plan variants (e.g., Choice POS II) into a relational format.
While top reviews are static, older reviews require traversing multiple pages. We paginate through the entire review history to build a complete sentiment corpus for each physician.
Healthtech platforms and telehealth startups augment their internal provider databases with external ratings, wait times, and updated clinic locations.
Insurance carriers analyse competitor networks by tracking which providers accept specific Medicare, Medicaid, or commercial plans in target ZIP codes.
Hospital systems and large group practices monitor patient reviews and sub-scores across their affiliated physicians to identify operational issues.
Care coordinators use wait time metrics and patient satisfaction scores to route patients to the highest-performing specialists.
Pharma reps use location data, hospital affiliations, and specialty focus to optimise territory mapping and target high-volume prescribers.
Analysts track provider density, specialty distribution, and patient sentiment trends across different metropolitan statistical areas (MSAs).
"Vitals holds one of the most comprehensive repositories of patient sentiment and provider credentials, but extracting it requires navigating aggressive rate limits and inconsistent directory structures."
Building a scraper for healthcare directories is straightforward; maintaining it at scale is not. Vitals employs strict IP rate limiting and frequent DOM changes. DataFlirt manages the proxy rotation, JavaScript rendering, and schema maintenance so your data engineering team receives clean, normalised provider data without the operational overhead.
Everything supported by our vitals.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 and pagination flows. Combined via scrapy-playwright middleware.
We maintain pools of US-based residential ISP proxies. Rotation happens per-request with strict concurrency limits to avoid triggering Vitals' rate-limiting heuristics.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About vitals.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available provider directories, locations, and reviews is generally permissible under US law, provided it does not involve authenticated patient portals or PHI (Protected Health Information). DataFlirt only extracts public data. We do not bypass login screens or access private medical records.
Yes. We can configure the pipeline to target specific taxonomies, such as all cardiologists in Texas, or iterate through the entire national directory.
Not all providers have complete data on Vitals (e.g., missing NPIs or insurance lists). Our schema enforces nullable fields where appropriate, and we provide data quality reports detailing fill rates for critical attributes.
We paginate through the entire review history for each provider, capturing the full corpus of patient feedback, dates, and sub-ratings.
Yes. If you provide a list of NPIs, we can use search heuristics to locate the corresponding Vitals profile and append the scraped review and location data to your existing records.
For large national directories, we typically run weekly or monthly refreshes. For targeted lists of high-priority providers, we can configure daily monitoring for new reviews or address changes.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of specialists in a single state or continuous monitoring of provider reviews nationwide - we scope, build, and operate the pipeline. Tell us what you need.
