net revenue
net_rev_amtDefinition
ISO-11179 Definition
The actual amount of revenue a healthcare organization expects to collect for services rendered after deducting contractual adjustments, charity care write-offs, and bad debt allowances from gross patient service revenue. Net revenue represents the realistic revenue amount that will be collected in cash and is the primary revenue metric used in healthcare financial reporting, budgeting, and performance management. The difference between gross revenue and net revenue is substantial in healthcare — large health systems may have gross-to-net ratios where they collect only 25 to 40 cents of every dollar of billed charges due to contractual adjustments.
Healthcare data teams calculate net_rev_amt by applying expected reimbursement rates from each payer contract to the mix of services delivered, tracking actual collections against net revenue expectations, analyzing gross-to-net ratios by payer and service line, and producing net revenue forecasts used in financial planning, capital budgeting, and strategic decision-making.
Standard Abbreviation
net_rev_amt
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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