claim denial reason
clm_denial_rsn_cdDefinition
ISO-11179 Definition
A standardized code assigned by a health insurance payer to explain why a healthcare claim or service line was denied, returned, or adjusted during adjudication. Denial reason codes follow standard code sets including CARC (Claim Adjustment Reason Codes) and RARC (Remittance Advice Remark Codes) maintained by the Washington Publishing Company and used in HIPAA 835 electronic remittance advice transactions. Common denial reason codes include CO-4 for incorrect procedure code, CO-11 for diagnosis inconsistent with procedure, CO-16 for missing or incorrect information, CO-29 for timely filing exceeded, and CO-97 for payment included in another service.
Healthcare data teams use clm_denial_rsn_cd as the primary dimension in denial analytics, grouping denials by reason code to identify systemic billing and coding issues, track denial trends over time, and prioritize process improvement initiatives with the highest revenue recovery potential.
Standard Abbreviation
clm_denial_rsn_cd
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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