zero pay
zero_pay_indDefinition
ISO-11179 Definition
A boolean indicator identifying a healthcare claim that was adjudicated and processed by a payer but resulted in zero payment due to full member cost-sharing responsibility, coordination of benefits where another payer is primary, or contractual terms that result in no plan payment for the billed service. Zero pay claims require careful handling in the revenue cycle — they must be distinguished from denials and processed correctly to generate patient statements for applicable cost-sharing amounts or to bill the secondary payer when COB applies. Incorrect processing of zero pay claims results in missed patient billing opportunities and failure to pursue secondary payer reimbursement.
Healthcare data teams use zero_pay_ind in claims analytics to categorize zero payment outcomes by reason, identify zero pays requiring patient billing versus secondary payer submission, track zero pay rates by payer and service type, and ensure zero pay accounts flow correctly through billing workflows without being incorrectly written off.
Standard Abbreviation
zero_pay_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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