Dataset

Complete Rate File

The same rates across all 350,000+ billing codes, not just drugs.

What do insurers pay across everything we bill?

The same table without the drug restriction: imaging, surgery, office visits, labs and equipment as well as drugs.

What you getRate distributions by insurer, state, code and setting

What it coversEvery billing code in the corpus. National.

What it is joined toRates, with Medicare and ASP+6% attached

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 state, every insurer

Every code every insurer publishes, in one state.

$8,000a year

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One insurer, every code

Everything a single insurer publishes, nationally. The only published price in this market is $25,000 for exactly this; ours is 28% under it.

$18,000a year

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One region, every insurer

Up to ten states across all 37 insurers.

$25,000a year

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Three insurers, every code

Any three insurers, nationally. Most competitive questions only need a handful.

$32,000a year

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Everything

All 353,270 codes, all 37 insurers, all 50 states and DC.

$60,000a year

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What is in it

All 350,000+ billing codes — imaging, surgery, office visits, labs, equipment and drugs
37 insurers across all 50 states and DC
Split by setting and by professional or institutional billing, so hospital and office pricing sit side by side
Each rate next to the Medicare allowed amount, and ASP+6% for drugs
A new build every quarter, in Parquet and CSV

Questions it answers

What does each insurer pay for a knee replacement in our metro area?
Where does our current fee schedule sit against the market, line by line?
Which service lines have the widest spread, and therefore the most room to negotiate?

What it does not do

It is not the cheaper way to get drug rates.

If you only care about infusion and specialty drugs, Specialty Drug Rates is a third of the price and adds the administration codes.

It will not tell you what one named practice is paid.

Same market-level resolution as every rate dataset here.

Who buys this

Health systems and physician groups modelling a full fee schedule
Analytics and billing software vendors that need a rate layer
Insurers benchmarking competitor networks

Why it is worth the money

One shape across every service

Imaging, surgery, office visits, labs, equipment and drugs all arrive in the same columns, so you write the query once.

Priced by what you actually need

Narrow it to a specialty, a region or a few insurers and the price comes down. You are not paying for rows you will never open.

Built to sit inside your product

The schema is versioned and every change is published, because a team building on this needs the columns to stay where they are.

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_market`

Every billing code, summarised by insurer, state and billing class. Full percentile spread, hospital charges alongside, and a quality score on each row.

Key payer × state × billing_code × billing_class

Column	Type	What it is
`payer` key	TEXT	The insurer.
`state` key	TEXT	Two-letter state.
`billing_code` key	TEXT	HCPCS or CPT. 353,270 distinct codes in the current build.
`billing_class` key	TEXT	professional or institutional.
`rate_count`	BIGINT	How many rates are behind the row.
`mean_rate`	DOUBLE	Arithmetic mean.
`median_rate`	DOUBLE	The middle rate.
`stddev_rate`	DOUBLE	Spread around the mean.
`p10`	DOUBLE	10 percent of rates fall below this.
`p25`	DOUBLE	25 percent fall below.
`p50`	DOUBLE	The median again, as a percentile.
`p75`	DOUBLE	75 percent fall below.
`p90`	DOUBLE	90 percent fall below.
`min_rate`	DOUBLE	Lowest observed.
`max_rate`	DOUBLE	Highest observed.
`chargemaster_median`	DOUBLE	What hospitals list for the same code, from their published prices.
`chargemaster_gross`	DOUBLE	Gross charge before any discount.
`chargemaster_hospitals`	BIGINT	How many hospitals are behind that figure.
`medicare_rate`	DOUBLE	Medicare allowed. Currently populated for procedures, not yet for drug codes.
`medicare_ratio`	DOUBLE	The rate over Medicare.
`payers_with_code`	BIGINT	How many insurers publish anything for this code. A 1 here means no comparison exists.
`cross_payer_median`	DOUBLE	The median across every insurer, so you can place one against the market.
`volume_score`	DOUBLE	Component of the quality score — how much data sits behind it.
`payer_agree_score`	DOUBLE	Component — how closely insurers agree.
`chargemaster_score`	DOUBLE	Component — consistency against hospital charges.
`medicare_score`	DOUBLE	Component — plausibility against Medicare.
`confidence_score`	DOUBLE	0 to 100, combining the four components above.
`outlier_flag`	BOOLEAN	Marks rows that look wrong. Shipped rather than deleted so you can see what we would have excluded.
`normalized_rate`	DOUBLE	The rate after unit correction, where one was applied.
`normalization_divisor`	DOUBLE	What it was divided by.
`normalization_ref_rate`	DOUBLE	The reference used.
`normalization_method`	TEXT	How it was corrected, e.g. asp_unit_correction for a per-vial filing.

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.

Most rows in the raw files are rates nobody would ever bill

Why it bitesPublished research on Transparency in Coverage data found the median insurer’s file was 95.7% ghost rates — provider and code pairs like a podiatrist priced for open-heart surgery. Across 61 insurers, 95.4% of pairs were ghosts. An average taken straight off a raw file is mostly noise.

How we handle itEvery row carries a distinct provider count and a 0 to 100 quality score, and we flag rather than delete, so you can set your own floor and see what you excluded. Our own published work uses 70 and above.

Some rates are a percentage, not a dollar amount

Why it bitesInsurers must publish a dollar figure wherever one can be calculated in advance. Where the contract is genuinely a percentage of billed charges, they publish the percentage instead. A 45 sitting next to a 4,500 is not a cheap version of the same thing.

How we handle itThe rate type travels with the value in every row. We never silently convert a percentage into dollars, and percentage rows are kept out of dollar comparisons rather than quietly averaged in.

A contracted rate is not what got paid

Why it bitesThese files describe what an insurer agreed to pay. They say nothing about deductibles, coinsurance, denials, retroactive adjustments or what actually landed on the remittance.

How we handle itWe say so on every page rather than in a footnote. If you need what was paid rather than what was agreed, this is the wrong dataset and we will tell you so before you buy it.

One insurer publishes many networks

Why it bitesA large insurer files rates for PPO, HMO, exchange, rental and administrative-services-only networks. Blending them produces a single rate that no provider is actually paid.

How we handle itNetworks stay distinct. Where you want one number we tell you which network it came from rather than averaging across products that are not comparable.

Averaging across modifiers gives a wrong number that looks right

Why it bitesJW and JZ change how drug wastage is billed. 26 and TC split a professional read from the equipment. Collapse them and the distribution shifts without any obvious sign.

How we handle itModifier is part of the key. The schema says so above the columns, and the sample is built so you can see the effect yourself.

Insurer names on multi-state files are not always what they seem

Why it bitesA file downloaded into one folder can carry a different company name inside it, which matters for plans operating across several states.

How we handle itWe are re-deriving names from file contents. Until that is finished every row carries a confidence column, so you can keep only the attribution you trust.

What to know before you buy

Coverage by code is uneven, because a code only appears where an insurer published a rate for it. Every row tells you how much is behind it.
Same insurer-attribution and point-in-time notes as the Infusion dataset.

Load it against your own model

Three states, the production columns, real rows, downloaded directly with no form in front of it. It is the fastest way to answer the fit question.

Download the free sample