claim denial
clm_denialDefinition
ISO-11179 Definition
A determination by a health insurance payer that a submitted healthcare claim or service line does not meet the criteria for reimbursement under the member benefit plan, resulting in non-payment of the billed amount. Claim denials are classified as hard denials that cannot be overturned without additional action and soft denials that can be appealed or corrected and resubmitted for payment. Common denial categories include eligibility denials when the member was not covered on the date of service, authorization denials when required prior approval was not obtained, coding denials when procedure or diagnosis codes are incorrect or unsupported, and timely filing denials when claims are submitted after the payer deadline.
Healthcare data teams build denial analytics pipelines that categorize denials by reason code, track denial rates by payer and service type, calculate the financial impact of outstanding denials, and prioritize the denial work queue by recovery opportunity to maximize revenue recovery.
Standard Abbreviation
clm_denial
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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