payment posting
pmt_postingDefinition
ISO-11179 Definition
The revenue cycle process of recording insurance payments, patient payments, contractual adjustments, and denial information received from payers and patients into the provider billing system to update account balances and identify remaining amounts owed. Accurate and timely payment posting is essential for revenue cycle performance — posting errors create incorrect account balances that misdirect collection efforts, delay identification of underpayments, and produce inaccurate financial reports. Electronic remittance advice processing through the HIPAA 835 transaction enables automated payment posting that eliminates manual data entry errors and accelerates the posting process.
Healthcare data teams measure payment posting performance through metrics including days from payment receipt to posting completion, electronic versus manual remittance processing rates, posting error rates, and unapplied payment aging to identify workflow bottlenecks and automation opportunities in the cash posting function.
Standard Abbreviation
pmt_posting
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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