Schema

Every column we sell

The complete column list for every dataset, taken from the production tables themselves rather than written up separately. Types and descriptions are what you would actually receive. Published up front, because whether this fits your model is a technical question and you should be able to answer it yourself.

key part of the primary key — read these first Every column below is in the current build. Nothing here is aspirational.

Read the key line first

Each table lists its key above the columns. Most trouble people hit with rate data comes from combining rows that should have stayed separate. In particular modifier is part of the key — averaging across modifiers gives you a number that looks fine and is wrong.

The tables

`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.

`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.

`coverage_cell`

One row per drug and insurer: whether prior authorization applies, what has to be tried first, which diagnoses are covered, and where the drug may be given.

Key payer_slug × drug × lob

Column	Type	What it is
`payer_slug` key	TEXT	The insurer. Joins to the rate tables.
`drug` key	TEXT	Active ingredient. Coverage rules are written at this level, not at code level.
`lob` key	TEXT	Line of business, taken from the policy and never inferred. Every row in the current build is commercial; Medicare Advantage, Medicaid and exchange are the same column and are not yet populated.
`prior_auth_required`	BOOLEAN	Whether approval is needed before the drug is given. True on 81% of rows.
`preferred_products`	TEXT[]	What the insurer wants tried first.
`products`	JSONB	Product-level detail where the policy distinguishes brands or biosimilars.
`covered_dx`	JSONB	Covered diagnoses with their ICD-10 codes and any dose ceiling, keyed by indication.
`site_of_care`	JSONB	Whether a site-of-care program applies and what it says.
`citations`	JSONB	The quote behind each field, so any value can be traced to a sentence.
`source_url`	TEXT	Direct link to the insurer’s own document.
`effective_date`	DATE	The insurer’s policy date. Present on 85% of rows.
`veracity`	TEXT	How the row was established: verified on 1,625 rows, corroborated on 798.
`confidence`	TEXT	Confidence in the extraction, high or medium. Nothing lower is shipped.
`signals`	JSONB	Supporting evidence found while reading the policy.
`attested_absent`	TEXT[]	Requirements we looked for in the policy and confirmed are not there — a step-therapy rule, a TB screen, a site-of-care program. A verified absence, not a gap. Non-empty on 1,577 of 2,423 rows.

`coverage_rule`

The individual rules behind each cell, each carrying the sentence it came from.

Key rule_id — one row per rule

Column	Type	What it is
`rule_id` key	TEXT	Primary key. One row per rule. A single drug-and-insurer pair routinely carries many rules of the same type — one policy states twenty separate clinical prerequisites — so the other columns do not form a key and joining on them fans rows out.
`payer_slug`	TEXT	The insurer. Joins to coverage_cell and to the rate tables.
`drug`	TEXT	Active ingredient.
`rule_type`	TEXT	covered_indication, step_therapy, site_of_care, quantity_limit, reauth, exclusion and so on.
`indication`	TEXT	What the rule is about, e.g. rheumatoid_arthritis. Empty where the rule applies to the drug generally rather than to one diagnosis.
`hcpcs`	TEXT	The billing code the rule attaches to, and the join to the rate tables. Present on 43% of rules: the rest are written about the drug rather than about a code, and we do not attach a code the policy did not name. At the cell level 76% carry at least one coded rule.
`value`	JSONB	The parsed rule — ICD-10 list, dose ceilings, required prior drugs, notes.
`citation_quote`	TEXT	The exact sentence from the policy. Never reworded.
`source_url`	TEXT	Link to the document the quote came from.
`effective_date`	DATE	When the policy version took effect.

`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.

See these columns with real values in them

The sample uses the production schema and real rows, so you can load it straight into your own model. It downloads directly, with no form in front of it.

Download the free sample