Dataset

Contracted Rates by Provider

What one named practice is contracted for, with which insurers, at what rates — down to the plan.

What is this practice actually contracted for, and at what rates?

The whole contract stack for a named practice: identity, the insurers it works with, the plans underneath them, and the rate on every code — with Medicare volume alongside so you can see which lines are real.

What you getOne row per provider, per plan, per code

What it coversNamed practices across the insurers we cover

What it is joined toRates, benchmarks, provider identity and observed Medicare volume

Buy the slice you need

You do not have to take the whole grid. Every slice is the same columns, the same quality gates and the same quarterly refresh over less of it — and slices stack, so nothing is ever bought twice.

One metro

Every provider in a defined market, with their contracts and rates.

$10,000a year

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One specialty, nationally

All providers in a specialty — infusion centres, retina, oncology.

$25,000a year

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Everything

The full provider grid across the insurers we cover.

$80,000a year

Get this

What is in it

Provider identity resolved end to end — 18.6 million tax-ID-to-provider links across 890,761 practices, with 1.47 million alternate names collapsed so one practice is one row
A flag for aggregator tax IDs, so a billing company is never mistaken for a practice
Every insurer and plan that practice holds a published contract with
The negotiated rate on every code under each of those contracts, with the rate type and arrangement carried alongside
Medicare volume for the same provider and code, so a contract line with no activity behind it is visible
340B status, specialty, metro and health system affiliation

Questions it answers

We are buying this infusion group. What are its contracted rates, and how do they compare with the market?
Which practices in this metro have the strongest commercial contracts for our drug?
Our own contracts — where are we below what comparable practices in the same market get?

What it does not do

It is not a claims dataset.

It shows what a practice is contracted for, not what it billed or was paid. Medicare volume is there as a reality check on the contract, not as revenue.

Volume is Medicare-observed only.

We can tell you what a practice bills Medicare. We cannot tell you its commercial volume, because no public file discloses it. Treat it as a floor and a sanity check, not a market share.

If you only need market ranges, this is overkill.

Rates summarised by insurer, state and code are in Specialty Drug Rates at a fifth of the price.

Who buys this

Investors and corporate development teams valuing a practice or a platform
Practice and health system leaders benchmarking their own contracts against comparable groups
Manufacturers mapping which practices in a market have the contracts to support their drug

Why it is worth the money

It answers the diligence question directly

When someone is buying a practice, the question is always what its contracts are worth. This is that question as a table rather than a four-week consulting engagement.

Plan level, not just insurer level

A practice can hold very different rates across one insurer’s PPO, HMO and exchange products. Rolling them into a single insurer rate hides the thing you are trying to see.

Ghost contract lines are marked, not hidden

Published research found the median insurer file is 95.7% provider and code pairs nobody would ever bill. Matching each line against observed Medicare volume is what separates a real contract from filing noise.

One name per practice

Group practices file under many spellings and several tax IDs. Normalizing that is most of the work in making provider data joinable to anything you already have.

What a row looks like

Real rows from the current build, in the shape this dataset ships.

Real rows · 2026-Q2-06 rate_market, the production table · as of 21 August 2026

Insurer	State	Rates	p10	Median	p90	Quality	Flagged
uhc	NY	6,221	$23.36	$33.23	$54.72	72.5	no
uhc	CA	6,092	$25.52	$34.83	$64.87	72.5	no
uhc	FL	5,923	$24.03	$32.99	$58.86	72.5	no
aetna	FL	5,611	$30.03	$1,591.69	$4,227.98	65.0	yes
uhc	TX	5,472	$24.41	$33.22	$57.82	72.5	no
aetna	CA	5,329	$31.64	$1,654.20	$4,295.29	65.0	yes

Denosumab, professional claims, six real rows out of the table you would license. Watch rows four and six: Aetna files this drug per vial where UnitedHealthcare files it per milligram, so the same drug reads as $1,591 instead of $33. The pipeline caught it, flagged the row and recorded how it was corrected — those rows ship flagged rather than deleted, so you can see what we would have excluded.

The columns

Published in full, before you talk to anyone. Whether this fits your model is a technical question and you should be able to answer it yourself.

`rate_intel`

Specialty and infusion rates at tax-ID level, with the ghost count, the confidence interval and the federal benchmark already on every row. This is the production table, column for column.

Key vintage × payer × state × billing_code × billing_code_modifier × billing_class × tin

Column	Type	What it is
`vintage` key	TEXT	Which quarterly build the row belongs to. Current is 2026-Q2-06.
`payer` key	TEXT	The insurer the rate is attributed to.
`src_payer`	TEXT	The file it came out of. Kept because one insurer’s file can carry rates for several.
`state` key	TEXT	Two-letter state.
`billing_code` key	TEXT	HCPCS or CPT.
`billing_code_modifier` key	TEXT	Part of the key. Averaging across modifiers gives a wrong number that looks right.
`service_code`	TEXT	Place-of-service grouping as the insurer filed it.
`billing_class` key	TEXT	professional or institutional.
`negotiated_type`	TEXT	negotiated, fee schedule, derived, or blank.
`tin` key	TEXT	Tax ID. This is what makes the table entity-level rather than market-level.
`n_obs`	BIGINT	Observations behind the row after filtering.
`n_raw`	BIGINT	Observations before filtering.
`n_ghost`	BIGINT	How many were ghost rates — codes that provider would never bill. Counted, not silently dropped.
`n_unit_uncertain`	BIGINT	Rows whose billing unit could not be resolved. Per-vial against per-milligram lives here.
`rate_raw`	DOUBLE	Before unit normalization.
`rate_final`	DOUBLE	After. This is the number to use.
`sd_used`	DOUBLE	Standard deviation behind the interval.
`ci_low`	DOUBLE	Lower bound of the confidence interval.
`ci_high`	DOUBLE	Upper bound. A wide interval is telling you something.
`veracity`	TEXT	measured where the rate is observed, otherwise how it was derived.
`proxy_source`	TEXT	What stood in when the rate was not directly observed.
`confidence`	DOUBLE	0 to 1. Set your own floor; ours is high.
`medicare_bench`	DOUBLE	Medicare allowed for the same code and locality.
`asp_per_unit`	DOUBLE	CMS average sales price. Drugs only, null for procedures.
`asp_plus6`	DOUBLE	ASP plus 6 percent, the federal drug benchmark.
`ratio_vs_medicare`	DOUBLE	The rate over the Medicare allowed amount.
`ratio_vs_asp6`	DOUBLE	The rate over ASP+6%. Under 1 means commercial pays less than the federal benchmark.
`anchor_source`	TEXT	What the row was anchored against, usually medicare.
`triangulation`	TEXT	Whether independent sources agreed.

`practice`

One row per tax ID, resolved to a single practice name. 890,761 practices.

Key tin

Column	Type	What it is
`tin` key	TEXT	Tax ID. Joins to the rate tables.
`practice_name`	TEXT	One canonical name, chosen across every spelling that appears.
`city`	TEXT	Primary location.
`state`	TEXT	Primary state.
`n_states`	BIGINT	How many states the practice operates in.
`n_clinicians`	BIGINT	How many providers bill under this tax ID.
`max_payers`	BIGINT	The most insurers any one of its providers is contracted with.
`evidence`	BIGINT	How much evidence sits behind the resolution.
`is_aggregator`	BOOLEAN	True for billing companies and management groups. Without this a biller looks like a practice.
`name_consensus`	DOUBLE	How strongly the sources agreed on the name.
`name_source`	TEXT	Where the chosen name came from.

`practice_member`

Which providers bill under which tax ID, and which insurers each one is contracted with. 18.6 million links.

Key tin × npi

Column	Type	What it is
`tin` key	TEXT	Tax ID.
`npi` key	TEXT	National Provider Identifier.
`n_payers`	BIGINT	How many insurers this provider is contracted with under this tax ID.
`payers`	TEXT	The insurers themselves, as a list. This is the answer to which insurers a practice accepts.

Traps in this data, and how we handle them

These catch people out whoever they buy from. They are worth knowing before you model anything with rate or coverage data, including ours.

Provider references in the files are not names

Why it bitesInsurers identify providers by reference numbers that mean nothing outside their own file, and the same practice appears under several tax IDs and spellings.

How we handle itWe match references to the national provider registry and normalize to one practice name, and we ship the raw name beside it so you can audit the match rather than trust it.

A contract line is not evidence of activity

Why it bitesMost provider and code pairs in these files are lines nobody would ever bill. At the provider level that matters more than at the market level, because a single ghost line can misprice a whole practice.

How we handle itEvery row carries observed Medicare volume for that provider and code, plus a flag where there is none, so dead lines are visible before they reach a model.

Bundled and capitated arrangements are not unit rates

Why it bitesA practice paid under a bundle or a capitated arrangement has no meaningful per-code rate, and treating one as though it does produces a number that looks fine and is wrong.

How we handle itThe arrangement type travels with every row, and bundled and capitated rows are excluded from unit-rate comparisons rather than averaged in.

340B changes the economics completely

Why it bitesTwo practices with identical contracts can have entirely different drug margins if one buys at 340B prices.

How we handle it340B status is on every row, sourced from the federal covered entity file, so you are never comparing a 340B practice with a non-340B one by accident.

Provider data is a moving target

Why it bitesPractices are acquired, tax IDs change, clinicians move. A profile is accurate as of the build it came from.

How we handle itEvery row carries its build quarter, and the changelog records what moved between builds.

What to know before you buy

This is the entity-level build and it is in development. The market-level rate datasets are available today; this one is not yet.
Coverage follows the same insurer set as the rate datasets, so the same six groups are missing.
Commercial volume is not available at any price, from anyone, because it is not disclosed. Only Medicare volume is observable.

This dataset is still in preview

Tell us what you need from it and we will tell you honestly where it is and when it lands.

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