underpayment
underpmt_amtDefinition
ISO-11179 Definition
The difference between the contracted reimbursement amount a healthcare provider is entitled to receive for a specific claim and the actual payment amount received from the payer, representing revenue owed but not yet collected. Underpayments occur when payers apply incorrect fee schedules, misclassify the procedure or service, apply incorrect member cost-sharing amounts, or make calculation errors in adjudication. Systematic underpayment by payers represents a significant and often undetected revenue loss — studies suggest healthcare providers recover only a fraction of underpaid amounts due to insufficient contract management and payment variance tracking.
Healthcare data teams build underpayment detection analytics that compare actual payment amounts against expected reimbursement calculated from contracted fee schedules, flag payment variances exceeding threshold amounts for follow-up, track underpayment recovery rates by payer, and calculate the total underpayment opportunity across the provider network.
Standard Abbreviation
underpmt_amt
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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