claim edit
clm_edit_cdDefinition
ISO-11179 Definition
A coded identifier for a specific automated validation rule applied during claim scrubbing or payer adjudication that checks a claim for compliance with billing guidelines, code validity, medical necessity criteria, or payer-specific requirements. Claim edits range from hard edits that reject claims outright for fundamental errors to soft edits that flag potential issues for human review. Common claim edit types include code validity edits checking procedure and diagnosis codes against current code sets, NCCI bundling edits identifying improperly unbundled services, age and gender edits validating procedure appropriateness for the patient demographics, and modifier edits validating that modifiers are used correctly for the billed service.
Healthcare data teams analyze clm_edit_cd distributions in pre-billing scrubbing reports to measure edit failure rates by type, identify providers or departments with high edit failure rates requiring education, track edit resolution rates and times, and calculate the revenue impact of claims held for edit resolution.
Standard Abbreviation
clm_edit_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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