claim frequency code
clm_freq_cdDefinition
ISO-11179 Definition
A single-digit code included on institutional claims indicating the type of claim being submitted — whether it is an original claim, a replacement of a prior claim, or a void of a prior claim. CMS and the National Uniform Billing Committee maintain the claim frequency code set used in Form Locator 4 of the UB-04 institutional claim form and the CLM05-3 element of the HIPAA 837I transaction. Common values include 1 for original admission claim, 7 for replacement of prior claim correcting a previously submitted and adjudicated claim, and 8 for void of prior claim canceling a previously paid claim.
Correct claim frequency code usage is essential for claim version management — using the wrong frequency code can result in duplicate payments, incorrect reprocessing, or failure to void erroneous claims. Healthcare data teams use clm_freq_cd in claims analytics to track resubmission volumes, identify facilities with high replacement claim rates suggesting billing accuracy issues, and monitor void claim activity for potential fraud or improper payment recovery.
Standard Abbreviation
clm_freq_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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