charge capture
chrg_captDefinition
ISO-11179 Definition
The process of accurately recording all billable healthcare services, supplies, and procedures delivered to a patient during a clinical encounter to ensure complete and compliant claim submission. Effective charge capture is the foundation of healthcare revenue integrity — services that are delivered but not captured result in permanent revenue loss that cannot be recovered after the timely filing deadline expires. Charge capture occurs through multiple mechanisms including automated charges triggered by orders in the electronic health record, manual charge entry by clinical staff, charge reconciliation comparing scheduled versus billed procedures, and charge capture audits identifying missing or undercoded services.
Healthcare data teams build charge capture analytics that compare volumes of ordered versus billed procedures by service type, identify providers with statistically low charge counts suggesting incomplete capture, and measure the revenue impact of charge capture improvement initiatives across clinical departments.
Standard Abbreviation
chrg_capt
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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