We extract Medicare Advantage plans, Part D formularies, Medigap pricing, provider directories, and CMS Star Ratings. 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 Medicare Advantage Plans objects from medicare.gov. All fields typed and schema-versioned.
"contract_id": "H1036", "plan_id": "062", "plan_name": "Humana Gold Plus HMO", "organization": "Humana", "premium": 0.0, "deductible": 0.0, "star_rating": 4.5, "max_out_of_pocket": 3400.0, "drug_coverage": true, "network_type": "HMO"
| # | contract_id | plan_id | plan_name | organization | premium | deductible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Part D Formularies objects from medicare.gov. All fields typed and schema-versioned.
"ndc_code": "00071101568", "drug_name": "LIPITOR 10 MG TABLET", "tier": "Tier 3", "prior_authorisation": false, "step_therapy": true, "quantity_limit": "30 per 30 days", "copay_preferred": 42.0, "copay_standard": 47.0, "plan_id": "014", "contract_id": "S5820"
| # | ndc_code | drug_name | tier | prior_authorisation | step_therapy | quantity_limit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Medigap Policies objects from medicare.gov. All fields typed and schema-versioned.
"policy_type": "Plan G", "company_name": "Mutual of Omaha", "premium_estimate_min": 112.0, "premium_estimate_max": 245.0, "age_rating": "Attained-age", "community_rating": false, "covers_part_a": true, "covers_part_b": true, "foreign_travel": true, "zip_code": "33101"
| # | policy_type | company_name | premium_estimate_min | premium_estimate_max | age_rating | community_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Provider Directory objects from medicare.gov. All fields typed and schema-versioned.
"npi": "1982736450", "first_name": "Sarah", "last_name": "Jenkins", "specialty": "Cardiology", "organization": "Heart Health Associates", "address": "1200 Medical Center Dr, Suite 400", "accepting_new_patients": true, "medicare_assignment": "Accepts Medicare Assignment", "telehealth_offered": true
| # | npi | first_name | last_name | specialty | organization | address |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Facility Quality objects from medicare.gov. All fields typed and schema-versioned.
"facility_id": "105034", "facility_name": "SUNSET VIEW NURSING HOME", "facility_type": "Skilled Nursing Facility", "overall_rating": 3, "health_inspection_rating": 2, "staffing_rating": 4, "quality_measure_rating": 5, "ownership_type": "For profit - Corporation", "certified_beds": 120
| # | facility_id | facility_name | facility_type | overall_rating | health_inspection_rating | staffing_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Medicare scraper processes complex plan benefits, provider networks, and formulary tiers across thousands of ZIP codes and county configurations.
Premiums, deductibles, maximum out-of-pocket limits, and benefit details across HMO, PPO, and PFFS plan types.
Tier structures, copays, coinsurance, step therapy requirements, and prior authorisation flags for every formulary.
Age-rated and community-rated premium estimates by ZIP code, age, and gender for all supplemental plan letters.
NPI-level extraction of doctors, specialists, and clinical groups accepting Medicare assignment.
Hospital Compare and Nursing Home Compare data, including CMS Star Ratings and inspection penalty records.
Plan availability mapping by county, ZIP code, and state-level boundaries to build coverage heatmaps.
Dual-eligible (D-SNP), Chronic Condition (C-SNP), and Institutional (I-SNP) specific benefit structures.
Estimated annual beneficiary costs based on varying health status and standard prescription drug profiles.
Run annual enrollment period (AEP) bulk exports or monitor mid-year plan changes continuously with diffs.
Brief in. Clean data out.
Provide target ZIP codes, plan types, or NPI lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for medicare.gov.
Schema validation, null-rate checks, and geographical coverage verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Medicare.gov relies on complex session state and geographical parameterisation. Here is how we extract data reliably.
Medicare.gov gates plan data behind geographical session states. We automate the injection of ZIP codes and county selections, maintaining sticky sessions to accurately surface localized plan availability without cross-contamination.
The Medicare Plan Finder and provider search tools are heavily JavaScript-rendered single-page applications. We run full Playwright browser sessions to execute JavaScript, trigger lazy-loading, and hydrate benefit widgets.
Part D formularies contain thousands of NDCs spread across deeply nested pagination structures. Our crawlers systematically traverse these paginated lists, mapping every drug back to its parent plan ID and tier structure.
CMS frequently updates the medicare.gov DOM structure, especially ahead of the Annual Enrollment Period. Our selector strategy uses fallback chains to ensure pipeline stability during critical Q4 data collection windows.
Extracting data across 40,000 ZIP codes triggers aggressive rate limiting. We distribute requests across a pool of US-based residential IPs, randomising request intervals to maintain high throughput without triggering blocks.
Health plans analyse competitor premiums, benefit structures, and network coverage to optimise their own plan designs.
Insurance brokerages ingest plan data to power proprietary quoting and enrollment platforms for their agents.
Pharmaceutical companies track formulary placement, tiering, and utilization management rules for their drug portfolios.
Health systems monitor physician affiliations and Medicare assignment status across competing networks.
Actuaries use historical premium and benefit data to forecast pricing trends and assess risk pools.
Researchers track facility quality ratings and plan availability to study healthcare access and outcomes.
"Medicare.gov holds the definitive record of US senior healthcare options, but extracting comparative data across 40,000 ZIP codes requires serious infrastructure."
Most teams underestimate the complexity of scraping Medicare.gov: stateful sessions, dynamic React components, and deeply nested formulary structures break simple HTTP clients. DataFlirt absorbs that complexity so your analysts can focus on benefit design — not proxy rotation.
Everything supported by our medicare.gov 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 required for plan finders.
We maintain pools of US residential ISP proxies. Rotation happens per-request with sticky sessions required for geographical targeting and ZIP code state management.
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 medicare.gov scraping, legality, and pipeline operations.
Ask us directly →Scraping public government data is generally permissible. DataFlirt targets only public, non-authenticated plan, provider, and facility data. We do not extract personal health information (PHI), circumvent authentication walls like MyMedicare, or violate HIPAA regulations. Clients should consult legal counsel for specific use cases.
We manage session state and inject ZIP/county parameters systematically. Our infrastructure iterates through a predefined list of geographic codes, establishing isolated browser sessions for each to prevent data contamination across regions.
Yes, we paginate through the complete drug list for every plan, capturing NDC codes, tier levels, prior authorisation rules, and quantity limits, mapping them directly to the specific contract and plan ID.
We scale our compute infrastructure to run full national refreshes daily during the Annual Enrollment Period (AEP), ensuring you have the latest premium and benefit adjustments as CMS publishes them.
Yes, we extract all facility metrics, CMS Star Ratings, staffing ratios, and health inspection penalty records available in the public provider directories.
Yes, our change detection diffs highlight premium or benefit adjustments outside of AEP. We can configure continuous pipelines that only emit records when a field value changes from the previous run.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off national plan dump or continuous monitoring of provider directories — we scope, build, and operate the pipeline. Tell us what you need.
