charge master
chrg_mstrDefinition
ISO-11179 Definition
A comprehensive price list containing the standard charges for every service, supply, procedure, and item that a healthcare organization provides, used as the starting point for claim billing before contractual adjustments are applied. Also known as the chargemaster or CDM (Charge Description Master), this database contains hundreds of thousands of line items for hospitals and serves as the basis for claim generation. Each chargemaster entry includes a description, revenue code, CPT or HCPCS code, and standard charge amount.
The CMS Price Transparency Rule effective January 2021 requires hospitals to publish their standard chargemaster prices and payer-negotiated rates publicly. Healthcare data teams maintain chrg_mstr databases that link charge codes to revenue codes, procedure codes, and contract rates, supporting charge capture workflows, claim generation, price transparency compliance, and net revenue modeling that projects actual collections based on payer mix and contracted rates.
Standard Abbreviation
chrg_mstr
Category
Production DDL — FACT_CLAIM_TRANSACTION
CREATE OR REPLACE TABLE FACT_CLAIM_TRANSACTION (
clm_txn_key INTEGER NOT NULL -- surrogate key,
clm_id VARCHAR(50) NOT NULL -- claim identifier,
mbr_key INTEGER NOT NULL -- FK to DIM_MEMBER,
prvdr_key INTEGER NOT NULL -- FK to DIM_PROVIDER,
clm_typ_cd VARCHAR(10) -- claim type code,
tot_chrg_amt DECIMAL(18,2) -- total charged amount,
tot_alwd_amt DECIMAL(18,2) -- total allowed amount,
tot_pd_amt DECIMAL(18,2) -- total paid amount,
cntrct_adj_amt DECIMAL(18,2) -- contractual adjustment,
denial_ind CHAR(1) -- denial indicator,
denial_rsn_cd VARCHAR(10) -- denial reason code,
prior_auth_nbr VARCHAR(30) -- authorization number,
clm_lag_days SMALLINT -- claim lag days,
days_ar SMALLINT -- days in AR,
load_dt TIMESTAMP_NTZ NOT NULL -- load timestamp
);
Standard Snowflake DDL for the canonical finance table. Convert to BigQuery or Databricks →
Why This Term Matters
Healthcare data terminology is foundational for any data engineer working in this industry. Precise understanding of standard terms enables accurate schema design, reduces downstream data quality issues, and ensures pipelines meet the regulatory and interoperability requirements imposed by HIPAA, HL7 FHIR, and CMS reporting frameworks. Without this foundation, even technically well-built pipelines produce data that fails validation when it reaches payers or regulators.
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