US Hospital Prices
What US hospitals actually charge - gross, discounted-cash AND payer-negotiated rates - for each billing code, in one tidy cross-hospital table. Since CMS's July-2024 mandate every hospital must publish a standardized machine-readable file (MRF) of its standard charges, but the files are a nightmare to use: 68 MB to 480 MB+ each, three CMS template variants (CSV 'tall', CSV 'wide', JSON), per-hospital column drift, prices as strings, and negotiated rates buried in nested payer arrays. We stream-parse a curated set of large hospitals that publish the CMS-conformant CSV-tall or JSON template and normalise everything to one schema: hospital, state, payer, plan, code type & code, item description, care setting, and the gross, discounted-cash, negotiated-dollar, min and max charges. Cleaning IS the product. Factual price data published under a federal mandate - freely reusable; shipped as a cleaned/derived aggregate, not the official files.
- Source
- HOSPITALPRICES
- Licence
- Redistributable open data
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- Gross, discounted-cash AND payer-negotiated dollar rates per billing code - one clean table
- 8 large US hospitals across 6 states (CA, NC, WI, AZ, ME, OH); CSV-tall + JSON MRF variants
- Payer × plan × code (CPT/HCPCS/DRG/NDC/RC/CDM) × setting - the join nobody wants to do by hand
- Prices parsed from strings to real USD; nested-JSON payer arrays flattened; min/max carried
- The tedious part done: 68-480 MB MRFs in three CMS templates → one tidy Parquet
- Payer/plan/price data only - no patients, no PII
- Published to satisfy the CMS Hospital Price Transparency federal mandate (45 CFR 180); factual data, freely reusable - shipped as a cleaned/derived aggregate
- Source: individual hospitals' machine-readable files (CMS Hospital Price Transparency rule)
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| hospital_id | str | Short hospital slug (registry id) | 100.0% | 8 |
| hospital_name | str | Hospital name | 100.0% | 8 |
| state | str | US state (2-letter) | 100.0% | 6 |
| payer_name | str | Payer / insurer name (null for gross/cash-only rows) | 97.7% | 36 |
| plan_name | str | Health plan name (null where not applicable) | 97.7% | 76 |
| code_type | str | Billing code system (CPT, HCPCS, MS-DRG, APR-DRG, NDC, RC, CDM, LOCAL) | 100.0% | 8 |
| code | str | Billing code for the item/service | 100.0% | 37,501 |
| description | str | Item / service description | 100.0% | 112,213 |
| setting | str | Care setting (inpatient, outpatient, both) | 100.0% | 5 |
| billing_class | str | Billing class (facility / professional), where reported | 64.1% | 2 |
| standard_charge_gross | float | Gross charge (chargemaster / list price), USD | 81.0% | 27,516 |
| standard_charge_discounted_cash | float | Discounted cash price (self-pay), USD | 81.8% | 30,209 |
| standard_charge_negotiated_dollar | float | Payer-specific negotiated charge, USD | 95.0% | 71,855 |
| standard_charge_min | float | Minimum negotiated charge across payers, USD | 97.5% | 41,935 |
| standard_charge_max | float | Maximum negotiated charge across payers, USD | 97.5% | 42,194 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: HOSPITALPRICES (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("us-hospital-prices_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
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