clean claim
cln_clmDefinition
ISO-11179 Definition
A healthcare insurance claim that contains all required data elements, passes all payer editing rules, and is accepted for adjudication on the first submission without rejection or request for additional information. Clean claims are processed and paid within mandated timeframes — CMS requires Medicare to pay clean claims within 30 days of electronic submission. The first-pass clean claim rate is a critical revenue cycle performance metric measuring what percentage of submitted claims are accepted without correction.
Industry leaders achieve clean claim rates above 95 percent while average performers may see rates of 85 to 90 percent. Each percentage point improvement in clean claim rate directly reduces rework costs and accelerates cash collection. Healthcare data teams calculate cln_clm rates by payer, facility, provider, and service type to identify systematic billing errors requiring coding education, registration workflow improvements, or payer-specific rule updates.
Standard Abbreviation
cln_clm
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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