We extract doctor profiles, clinic intelligence, consultation fees, patient reviews, and health Q&A from Lybrate. 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 lybrate.com. All fields typed and schema-versioned.
"doctor_id": "DOC-88392", "name": "Dr. Arun Kumar", "specialty": "Cardiologist", "experience_years": 15, "education": "MBBS, MD", "registration_number": "45992-MCI", "consultation_fee": 800, "city": "Delhi"
| # | doctor_id | name | specialty | experience_years | education | registration_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Clinic Intelligence objects from lybrate.com. All fields typed and schema-versioned.
"clinic_id": "CLN-1029", "clinic_name": "Apollo Sugar Clinics", "doctor_count": 4, "address": "Koramangala 8th Block", "city": "Bengaluru", "pincode": "560095", "operating_hours": "09:00 to 21:00", "rating": 4.5
| # | clinic_id | clinic_name | doctor_count | address | city | pincode |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Patient Reviews objects from lybrate.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "doctor_id": "DOC-88392", "patient_name": "Amit S.", "rating": 5.0, "review_text": "Very patient doctor. Listened to all my issues carefully.", "review_date": "2023-10-12", "wait_time_rating": 4.5, "recommendation_flag": true
| # | review_id | doctor_id | patient_name | rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Health Q&A objects from lybrate.com. All fields typed and schema-versioned.
"question_id": "Q-44102", "topic": "Dermatology", "question_text": "Experiencing severe hair fall for 3 months. What to do?", "patient_age": 28, "patient_gender": "Male", "doctor_id": "DOC-11234", "answer_date": "2023-09-01", "upvotes": 12
| # | question_id | topic | question_text | patient_age | patient_gender | doctor_answer |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Availability objects from lybrate.com. All fields typed and schema-versioned.
"doctor_id": "DOC-88392", "clinic_id": "CLN-1029", "date": "2023-11-20", "time_slots": "['10:00', '10:30', '11:00', '16:00']", "fee_online": 500, "fee_offline": 800, "booking_type": "Instant", "next_available_slot": "2023-11-20T10:00:00Z"
| # | doctor_id | clinic_id | date | time_slots | fee_online | fee_offline |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Lybrate scraper handles every layer of the platform: doctor directories, clinic mappings, dynamic fee structures, patient sentiment, and the Q&A corpus. Built with JavaScript rendering and anti-bot circumvention.
Extract qualifications, experience years, registration numbers, specialties, and professional statements across all medical categories.
Capture clinic addresses, operating hours, facility photos, and map multiple doctors to single clinic entities.
Track online consultation fees versus in-person clinic fees, timestamped per crawl to monitor pricing trends.
Full review text, star ratings, wait time scores, and recommendation flags paginated across all doctor profiles.
Extract public patient questions, demographics, and verified doctor responses to build extensive medical NLP datasets.
Monitor next available slots and booking availability windows for high-demand specialists.
Target extraction by city, locality, or pincode to map healthcare density in specific regions.
Navigate taxonomy across Ayurveda, Homeopathy, Dentistry, Cardiology, and 50+ other medical specialties.
Run one-off directory exports or configure continuous pipelines with change-detection diffing for fee updates.
Brief in. Clean data out.
Provide target cities, medical specialties, or specific doctor URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for lybrate.com.
Schema validation, null-rate checks, and data type verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Healthcare directories employ strict rate limiting and dynamic rendering. Here is how we stay resilient.
Lybrate limits aggressive scraping via IP blocks. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing, trained on real user behaviour patterns.
Clinic tabs, availability slots, and paginated reviews are heavily JavaScript-rendered. We run full Playwright browser sessions to trigger lazy-loads and capture data that headless HTTP clients miss entirely.
Profile layouts vary between premium doctors and standard listings. Our selector strategy uses multiple fallback chains per field so structural variations do not break your data pipeline.
For large directories, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and coverage drops, responding before you notice.
Telemedicine platforms monitor Lybrate consultation fees and doctor acquisition to adjust their own pricing and recruitment strategies.
Analysts track specialist distribution across cities and localities to identify underserved medical markets.
ML teams use the extensive health Q&A corpus to train healthcare LLMs, symptom checkers, and medical chatbots.
Pharma companies and medical device manufacturers build targeted outreach lists based on doctor specialty, clinic size, and location.
Health insurance providers verify doctor credentials, registration numbers, and active practice locations for network compliance.
Hospital chains aggregate patient reviews and wait-time feedback to benchmark clinic performance against independent practitioners.
"Lybrate holds India's most structured repository of doctor credentials, patient sentiment, and consultation pricing: critical data for healthcare analytics."
Extracting healthcare directories at scale requires bypassing regional rate limits and rendering dynamic clinic profiles. DataFlirt handles the proxy rotation, session management, and schema maintenance, delivering structured medical intelligence directly to your warehouse.
Everything supported by our lybrate.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 Indian regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 lybrate.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Lybrate is generally permissible. DataFlirt targets only public, non-authenticated doctor profiles, clinic details, and public Q&A data. We do not extract personal patient data, private consultations, or violate privacy regulations. Clients should review terms of service and consult legal counsel for specific use cases.
We use Indian residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 403/CAPTCHA rate spikes in real time and trigger pool rotation automatically.
Full directory refreshes at a weekly or monthly cadence complete within a defined window depending on target size. Specific subsets, like fee monitoring for a target list of 10,000 doctors, can run daily.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per doctor for online and offline consultation fees from the date your pipeline starts.
Our smallest packages start at a defined target list (typically one city or specialty) with weekly delivery. For full national directory extraction, we price based on volume and delivery frequency.
Yes, including full pagination across all patient reviews on a doctor profile. Each review record includes rating, text, wait time feedback, and recommendation status.
Absolutely. We provide a sample run of up to 500 doctor profiles or clinic records as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full directory dump or continuous fee monitoring across 100K doctors, we scope, build, and operate the pipeline. Tell us what you need.
