claim submission
clm_submDefinition
ISO-11179 Definition
The process of transmitting a completed healthcare claim to the appropriate insurance payer for adjudication and payment, using standardized electronic transaction formats or paper claim forms. Electronic claim submission through HIPAA-compliant 837 professional, institutional, or dental transaction sets is required for most Medicare and Medicaid billing and strongly preferred by commercial payers for faster processing and payment. Claims may be submitted directly to payers, through a clearinghouse that validates and routes claims to multiple payers, or through a practice management system with integrated claims submission functionality.
Timely claim submission following charge capture is critical to cash flow management and avoidance of timely filing denials. Healthcare data teams track clm_subm metrics including submission lag days from date of service to submission date, electronic versus paper submission rates by payer, clearinghouse rejection rates identifying systemic claim preparation errors, and same-day submission rates for time-sensitive claim types.
Standard Abbreviation
clm_subm
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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