payer audit
pyr_audit_indDefinition
ISO-11179 Definition
A boolean indicator identifying that a healthcare claim or provider has been selected for review by an insurance payer to verify that billed services were medically necessary, correctly coded, and supported by adequate clinical documentation. Payer audits include pre-payment reviews that hold claims pending documentation submission, post-payment audits that recoup previously paid claims found to be unsupported, and focused medical reviews targeting specific procedure types or provider billing patterns. Commercial payer audits are governed by contract terms while government payer audits including RAC, ZPIC, and MAC reviews are governed by program integrity regulations.
Healthcare data teams maintain pyr_audit_ind tracking systems that monitor audit request volumes by payer and claim type, track documentation submission deadlines, manage appeal rights for unfavorable audit determinations, and analyze audit finding patterns to identify systemic billing or documentation issues requiring compliance program intervention.
Standard Abbreviation
pyr_audit_ind
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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