insurance verification
ins_verifDefinition
ISO-11179 Definition
The process of confirming a patient active insurance coverage, benefit details, and cost-sharing obligations with the health insurance payer before or at the time of a healthcare service to ensure billing accuracy and inform patients of their financial responsibility. Insurance verification involves contacting the payer through electronic eligibility inquiry transactions, provider portals, or telephone to confirm coverage effective dates, plan type, deductible amounts and year-to-date accumulation, copay and coinsurance requirements, out-of-pocket maximum status, and any benefit limitations or exclusions relevant to the planned service. Effective insurance verification prevents eligibility-related claim denials, enables accurate patient cost estimates, and supports point-of-service collection of known patient obligations.
Healthcare data teams measure ins_verif performance through eligibility denial rates, verification turnaround times, and the percentage of encounters verified before service to evaluate workflow efficiency and identify payers or service types with high eligibility denial rates.
Standard Abbreviation
ins_verif
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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