remittance advice
remit_advDefinition
ISO-11179 Definition
A document sent by a health insurance payer to a healthcare provider explaining how a claim was processed, including the amount paid, any adjustments applied, denial reasons for unpaid services, and member cost-sharing amounts. The HIPAA standard electronic remittance advice is the 835 transaction set which enables automated payment posting in provider billing systems. Remittance advice contains Claim Adjustment Reason Codes and Remittance Advice Remark Codes that explain payment decisions at the claim and service line level.
Accurate and timely remittance advice processing is essential for revenue cycle efficiency — automated 835 posting eliminates manual payment entry, accelerates cash posting, and enables systematic denial tracking. Healthcare data teams build remittance processing pipelines that parse 835 transactions, map CARC and RARC codes to denial categories, automate payment posting, and generate denial work queues prioritized by recovery opportunity and payer appeal deadlines.
Standard Abbreviation
remit_adv
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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