claim rebill
clm_rebillDefinition
ISO-11179 Definition
The process of resubmitting a previously denied, rejected, or incorrectly processed healthcare claim to a payer after correcting the identified errors or providing additional documentation to support payment. Claim rebilling is a core revenue cycle activity that recovers revenue from initially denied claims and is distinguished from appeals in that rebills involve correcting factual errors while appeals challenge payer clinical or coverage determinations. Common rebill scenarios include correcting diagnosis or procedure codes, updating patient demographic or insurance information, adding missing modifiers, attaching supporting clinical documentation, and resubmitting claims that were rejected due to technical errors.
Healthcare data teams track clm_rebill volumes and success rates by denial reason code, measure the average number of submission attempts required to achieve payment by payer and claim type, calculate the administrative cost of rework per claim, and identify high-volume rebill categories where upstream process improvements could prevent initial denials.
Standard Abbreviation
clm_rebill
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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