We extract local pharmacy pricing, drug catalogues, dosage variants, and home delivery rates from Blink Health. 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 Drug Profiles objects from blinkhealth.com. All fields typed and schema-versioned.
"drug_name": "Lisinopril", "generic_name": "Lisinopril", "rx_required": true, "drug_class": "ACE Inhibitor", "active_ingredients": "['Lisinopril']", "brand_name": "Prinivil", "ndc_code": "00006-0035-54"
| # | drug_name | generic_name | brand_name | ndc_code | rx_required | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Variants objects from blinkhealth.com. All fields typed and schema-versioned.
"drug_id": "d-8492", "form": "Tablet", "dosage": "10mg", "quantity": 30, "blink_price": 4.95, "retail_price": 18.0, "discount_pct": 72, "home_delivery_price": 4.95
| # | drug_id | form | dosage | quantity | blink_price | retail_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Local Pharmacy Availability objects from blinkhealth.com. All fields typed and schema-versioned.
"pharmacy_name": "Walgreens", "chain_name": "Walgreens", "city": "Austin", "state": "TX", "zip_code": "78701", "accepts_blink": true, "distance_miles": 1.2, "phone": "512-555-0199"
| # | pharmacy_id | pharmacy_name | chain_name | address | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Home Delivery Details objects from blinkhealth.com. All fields typed and schema-versioned.
"drug_id": "d-8492", "delivery_available": true, "shipping_cost": 0.0, "estimated_days": "2-3", "refill_eligible": true, "auto_refill_discount": 5, "requires_signature": false
| # | drug_id | delivery_available | delivery_provider | estimated_days | shipping_cost | refill_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from blinkhealth.com. All fields typed and schema-versioned.
"keyword": "cholesterol", "position": 1, "drug_name": "Atorvastatin", "is_generic": true, "lowest_price": 9.95, "most_common_prescription": "20mg, 30 tablets", "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | position | drug_name | is_generic | lowest_price | most_common_prescription |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Blink Health scraper handles every layer of the platform: drug profiles, dynamic pricing matrices, local pharmacy networks, and home delivery logistics - with JavaScript rendering and geo-targeted session management built in.
Extract complete drug profiles including generic names, brand equivalents, descriptions, side effects, and active ingredients.
Capture the discounted Blink Price versus estimated retail price across all available forms, dosages, and quantities.
Scrape local pharmacy availability, chain affiliation, address data, and distance metrics based on input zip codes.
Track home delivery availability, shipping costs, estimated transit times, and auto-refill discount structures.
Extract and map pricing differentials between brand-name medications and their generic equivalents.
Scrape search results for specific medical conditions to map related drug recommendations and their starting prices.
Iterate through every combination of form (tablet, capsule, liquid), dosage, and quantity to build a complete pricing matrix.
Run continuous pipelines at daily or weekly cadences to track prescription price fluctuations over time.
Bypass rate limits and CAPTCHAs using residential proxies and TLS fingerprint spoofing to ensure uninterrupted extraction.
Inject specific zip codes or coordinates into the session to extract hyper-local pharmacy networks and pricing tiers.
Brief in. Clean data out.
Provide drug lists, conditions, or zip codes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for blinkhealth.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Healthcare platforms restrict automated access to pricing data. Here is how we maintain resilient extraction pipelines.
Pharmacy availability and pricing depend on location. We manage concurrent sessions across thousands of US zip codes, isolating cookies to ensure accurate local data extraction.
A single drug can have hundreds of form, dosage, and quantity combinations. Our crawlers systematically iterate through these React-driven dropdowns using Playwright to extract the full pricing matrix.
Healthcare sites aggressively block data centre IPs. We route all requests through US-based residential ISP proxies with realistic browser fingerprints and randomised request timing.
We utilise multiple fallback chains per field - CSS selectors, XPath, and intercepted API responses - ensuring layout changes do not break your pricing feeds.
We maintain a hash index of last-seen prices. Subsequent runs only push diffs, reducing storage bloat and downstream processing load.
Digital pharmacies and telehealth providers monitor Blink Health pricing to adjust their own cash-pay rates.
Pharma analysts track generic discount depths and cash-pay market dynamics outside traditional insurance channels.
Researchers and consumer advocates aggregate prescription pricing data to track out-of-pocket healthcare costs.
Analyse the geographic footprint of participating pharmacies and local availability of specific medications.
Telemedicine platforms ingest pricing data to show patients estimated medication costs during consultations.
Train machine learning models on drug classifications, side effects, and pricing correlations.
"Prescription pricing in the US is notoriously opaque. Blink Health provides a critical window into cash-pay rates, but only if you can extract the data at scale."
Scraping healthcare platforms requires navigating geo-fenced pricing, complex dosage matrices, and aggressive anti-bot measures. DataFlirt manages the entire extraction infrastructure - from proxy rotation to schema maintenance - delivering clean, normalised drug data directly to your warehouse.
Everything supported by our blinkhealth.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 and deduplication. Playwright manages JavaScript rendering and complex UI interactions for dosage matrices.
We maintain pools of US-based residential proxies, allowing us to simulate local users across thousands of zip codes for accurate local pricing.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About blinkhealth.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and pharmacy data is generally permissible under applicable law. DataFlirt targets only public, non-authenticated drug catalogues and cash-pay rates. We do not extract protected health information (PHI), patient records, or violate HIPAA regulations.
We use geo-targeted US residential proxies and inject specific zip codes into the browser session. This allows us to map local participating pharmacies and calculate accurate distance metrics.
Yes. Our Playwright crawlers systematically select every available form, dosage, and quantity to build a complete pricing matrix for each drug.
We use US-based residential proxies, full Playwright browser sessions with realistic TLS fingerprints, and randomised request timing. We monitor for CAPTCHA rate spikes and trigger automated solver queues when necessary.
We can configure pipelines to run daily, weekly, or on custom cadences. Full catalogue refreshes typically complete within a 4-8 hour window depending on the required depth of dosage permutations.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per drug and dosage variant, allowing you to track Blink Price fluctuations historically.
Yes. We provide a sample run of up to 100 drugs or a specific therapeutic class as part of the pre-engagement scoping process, allowing you to validate data quality and schema fit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off drug catalogue dump or continuous price monitoring across thousands of zip codes - we scope, build, and operate the pipeline. Tell us what you need.
