Trillions of Negotiated Rates via Delta Sharing, Direct to Your Lakehouse
No ETL. No file transfers. Accept the share and query real payer rates with SQL, Python, or Spark.
The full picture, queryable in Databricks
We process every published machine-readable file and share the structured output via Delta Sharing. Same data powering our UI and API.
of Negotiated Rates
Commercial Payers
Filter Columns
Data Refreshes
Your lakehouse. Our data. No pipeline.
We share structured MRF data via Delta Sharing. You query it like any other Delta table.
Zero-pipeline access
Accept the Delta Share. Tables appear in your Unity Catalog — no ETL, no file transfers, no data duplication.
100+ filterable columns
NPI, TIN, payer, plan, network, taxonomy, billing code, modifier, place of service, state — and more. Filter and join against your own data however you need.
Flexible views for common queries
Pre-built views for latest month, rolling three-month windows, and full historical data. Use them as-is or build your own.
Built for teams that run on Databricks
If your team already works in Databricks, the data is right where you need it.
BI Dashboards
Connect Tableau, Looker, or Power BI directly to shared Delta tables.
Custom Analytics
Query 1T+ rates with SQL or PySpark. Join with your own data.
Replace Manual Reports
Replace spreadsheets with automated rate comparisons.
Internal Tools
Power internal apps and workflows with live rate data.
Rate Benchmarking
Compute percentiles by payer, state, specialty, and billing code.
Fee Schedule Management
Build and update fee schedules grounded in real market data.
MRF data, delivered to your lakehouse
Commercial payers publish machine-readable files with their negotiated provider rates. The problem is the files are enormous JSON documents, hundreds of gigabytes per payer, and turning them into something queryable takes real engineering work.
PayerPrice handles that pipeline and delivers clean, structured tables to your Databricks environment via Delta Sharing. Accept the share through Unity Catalog and you're querying with SQL or PySpark.
Why Databricks teams use this
You already have the compute and the tooling. What you probably don't have is a clean feed of payer rate data that fits natively into your lakehouse architecture.
Building a custom MRF pipeline is a multi-month project with ongoing maintenance. PayerPrice's data share gives your team current, structured tables, refreshed monthly without adding another pipeline to babysit.
What the data covers
Contractual negotiated rates from over 200 commercial payers including UnitedHealthcare, Elevance (Anthem), Aetna, and Cigna. Rates for every billable code type: CPT, HCPCS, MS-DRG, Revenue Codes, and more.
Filter across 100+ columns including NPI, TIN, payer, plan, network, taxonomy code, and geography down to zip code. Medicare percentage benchmarks are included so you can run percentile positioning and percent-of-Medicare comparisons in the same environment.
How teams use it
Revenue cycle teams build automated pipelines comparing contracted rates against remittance data to catch underpayments and flag rate degradation, sometimes piping results into Tableau or Power BI for the contracting team.
The highest-value pattern: joining payer rates with internal claims and volume data in the same lakehouse. Fee schedule optimization, market expansion modeling, M&A diligence.
Also available via REST API or Snowflake Data Share.
Delta Sharing — open protocol
Standard Delta Sharing tables. No proprietary connectors, no vendor lock-in. Query with SQL, Python, Spark, or any Delta Sharing client.
Monthly data refreshes
New data shared within days of payer publication, every month. No action on your side.
Built-in Medicare benchmarks
Every rate includes a Medicare percentage for easy comparison across payers.
Compute on your account
Queries run on your Databricks compute. You control the cluster size, runtime, and cost.
Real rates. Standard SQL.
Query negotiated rates directly from your Databricks lakehouse.
SELECT
payer,
billing_code,
state,
PERCENTILE_CONT(0.50) WITHIN GROUP (
ORDER BY negotiated_rate
) AS p50,
PERCENTILE_CONT(0.75) WITHIN GROUP (
ORDER BY negotiated_rate
) AS p75,
PERCENTILE_CONT(0.90) WITHIN GROUP (
ORDER BY negotiated_rate
) AS p90
FROM negotiated_rates
WHERE
state = 'MA'
AND taxonomy_code = '208000000X'
AND billing_code IN ('99213', '99214')
GROUP BY payer, billing_code, state;payer | billing_code | p50 | p75 | p90
----------+--------------+--------+--------+-------
United | 99213 | 149.83 | 212.53 | 241.98
United | 99214 | 214.75 | 307.10 | 349.66
Aetna | 99213 | 142.10 | 198.44 | 229.87
Aetna | 99214 | 201.33 | 289.62 | 334.15
Cigna | 99213 | 155.20 | 219.81 | 248.32
Cigna | 99214 | 222.47 | 315.88 | 358.91
(6 rows)“In one instance, we were prepared to accept sub-optimal rates based on flawed rate assumptions, and reversed course once we got access to PayerPrice.”
Real negotiated rates. One query away.
Accept the Delta Share and start querying trillions of contracted rates in your lakehouse. No ETL required.
Free trial access. No credit card required.
Databricks Data Share FAQs
Your questions answered
We send you a Delta Sharing invitation. You accept it in your Databricks workspace and the tables appear in Unity Catalog. Most teams are live in under a day.
We process new machine-readable files from payers every month. Updated data appears in the share within days of payer publication. No action needed on your side.
The share includes Delta tables for negotiated rates, provider-payer mappings, and plan metadata. Over 100 columns including NPI, TIN, payer, plan, network, taxonomy, billing code, modifier, place of service, and state. Pre-built views cover common query patterns.
Yes. We can provision a share scoped to the payers, states, or specialties you need. This keeps the data volume manageable and the cost predictable.
Pricing is based on the scope of data shared. You pay for the Databricks compute you use to query it. Contact us for pricing details.
Yes. We retain historical MRF snapshots so you can track rate changes over time. The historical depth depends on your plan.
You do. Queries run on your Databricks clusters, so you control the size, runtime, and cost. The data share itself is read-only.
The share includes commercial payer negotiated rates from published MRFs — currently 200+ payers. It does not include Medicare fee schedules (those are public and free), Medicaid rates, or payers that have not yet published compliant MRFs.
Can't find what you're looking for? Contact our data team