We extract doctor profiles, clinic affiliations, lab test pricing, and pharmacy catalogues from MFine. 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 mfine.co. All fields typed and schema-versioned.
"doctor_id": "DOC-84921", "name": "Dr. Ramesh Kumar", "specialty": "Cardiology", "qualifications": "MBBS, MD - General Medicine, DM - Cardiology", "experience_years": 14, "consultation_fee": 800.0, "rating": 4.8
| # | doctor_id | name | specialty | qualifications | experience_years | languages_spoken |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lab Tests objects from mfine.co. All fields typed and schema-versioned.
"test_id": "LAB-3921", "test_name": "Comprehensive Full Body Checkup", "category": "Health Packages", "price": 1499.0, "fasting_required": true, "report_turnaround_time": "24 hours", "home_collection_available": true
| # | test_id | test_name | category | parameters_covered | fasting_required | home_collection_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pharmacy objects from mfine.co. All fields typed and schema-versioned.
"sku_id": "MED-99214", "medicine_name": "Pan 40 Tablet", "manufacturer": "Alkem Laboratories Ltd", "mrp": 155.5, "selling_price": 132.17, "prescription_required": true, "stock_status": "In Stock"
| # | sku_id | medicine_name | manufacturer | active_ingredients | packaging_size | prescription_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Clinics & Hospitals objects from mfine.co. All fields typed and schema-versioned.
"clinic_id": "CLN-4829", "clinic_name": "Apollo Clinic", "city": "Bengaluru", "locality": "Koramangala", "specialties_offered": "['General Physician', 'Pediatrics', 'Gynecology']", "doctor_count": 12, "rating": 4.6
| # | clinic_id | clinic_name | city | locality | pin_code | specialties_offered |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from mfine.co. All fields typed and schema-versioned.
"search_query": "Dermatologist", "location_pin": "560034", "rank_position": 1, "entity_name": "Dr. Sneha Reddy", "primary_attribute": "Dermatology", "price_indicator": 600.0, "sponsored_flag": false
| # | search_query | location_pin | result_type | rank_position | entity_id | entity_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our MFine scraper handles every layer of the platform: doctor directories, diagnostic test pricing, pharmacy catalogues, and pin code serviceability with residential proxies and dynamic hydration handling built in.
Extract qualifications, experience, spoken languages, specialties, and consultation fees for thousands of listed practitioners.
Capture individual test costs, package inclusions, fasting requirements, and turnaround times across different diagnostic partners.
Track medicine MRPs, selling prices, active ingredients, and prescription requirements across the entire pharmacy inventory.
Inject pin code specific headers and cookies to capture accurate hyperlocal pricing and serviceability flags.
Monitor next available consultation times and home collection slots to gauge provider capacity and demand.
Map doctors to physical clinics, extracting hospital amenities, bed counts, and aggregate patient ratings.
Track organic versus sponsored positions for specialty keywords across different city pin codes.
Run continuous pipelines that emit diffs for consultation fee adjustments or medicine price drops.
Scale extraction across Bengaluru, Delhi, Mumbai, and tier-2 cities concurrently without triggering rate limits.
Brief in. Clean data out.
Provide target specialties, pin codes, or test categories. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for mfine.co.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Healthcare platforms employ strict rate limits and geo-fencing. Here is how we stay resilient and deliver clean normalisation.
MFine alters doctor availability, lab test pricing, and medicine stock based on location. We inject specific pin codes into session cookies and API headers to extract accurate hyperlocal data.
Many provider lists and pricing widgets on MFine load dynamically via client-side JavaScript. We use Playwright to execute these scripts and wait for network idle states before parsing the DOM.
Aggressive scraping triggers IP bans. We route all requests through Indian residential proxy networks, rotating IPs per request to mimic organic user distribution across regions.
Doctor qualifications and hospital names are often entered inconsistently. Our pipeline applies regex-based normalisation rules to clean and standardise these fields before warehouse delivery.
If MFine changes its API response structure for consultation fees, our pipeline detects the resulting null values, halts the sync, and alerts our engineering team immediately.
Healthcare aggregators track lab test and medicine prices to maintain competitive parity in local markets.
Health-tech startups identify specialty gaps by pin code to target their provider acquisition efforts.
Insurers cross-reference doctor availability and consultation fees to optimise their cashless network directories.
Pharmaceutical companies track active ingredients, generic alternatives, and discount structures across the platform.
Machine learning teams train triage models using symptom-to-specialty mappings derived from provider profiles.
Diagnostic chains analyse test provider density by locality to plan new physical collection centers.
"MFine aggregates India's fragmented healthcare supply into a single digital interface. Without automated extraction, monitoring this pricing and availability is impossible."
Most teams underestimate the investment required: reliable MFine scraping requires residential proxies, pin-code based session handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our mfine.co 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 and ECS. 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 mfine.co scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from MFine is generally permissible under applicable law. DataFlirt targets only public, non-authenticated doctor profiles, lab test pricing, and pharmacy catalogues. We do not extract patient records, circumvent authentication walls, or violate privacy regulations.
We inject specific pin codes into session cookies and API headers. This ensures the pipeline captures accurate hyperlocal pricing, doctor availability, and medicine stock for the exact regions you target.
Yes. The pipeline extracts the next available consultation slots for each doctor, allowing you to monitor provider capacity and demand trends over time.
We extract medicine names, manufacturers, active ingredients, packaging sizes, MRP, selling prices, discount percentages, and prescription requirements across the entire catalogue.
Pipelines can be configured for daily, weekly, or monthly cadences. Full catalogue refreshes typically complete within a 6 to 12 hour window depending on the target scope.
We use Indian residential proxy pools, rotate IPs per request, and enforce strict concurrency limits. Our request timing is modelled on human behaviour to prevent triggering automated rate limiters.
Yes. We provide a sample run of up to 500 records as part of the pre-engagement scoping process so you can validate schema fit and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of doctor profiles or a continuous price-monitoring feed for lab tests, we scope, build, and operate the pipeline. Tell us what you need.
