gross collection rate
gross_coll_rtDefinition
ISO-11179 Definition
The percentage of a healthcare organization total gross charges that is collected as payment from all sources including insurance payers and patients, calculated before deducting contractual adjustments and write-offs. Gross collection rate is a raw measure of total payment received relative to billed charges and reflects both the provider charge structure and the reimbursement rates received from the payer mix. Because gross charges bear little relationship to actual collectible revenue in most healthcare markets due to large contractual adjustments, gross collection rate is a less meaningful performance indicator than net collection rate but remains useful for tracking trends and comparing performance across time periods with similar payer mixes.
Healthcare data teams calculate gross_coll_rt alongside net collection rate to provide a complete picture of revenue cycle performance, track gross collection rate trends to identify shifts in payer mix or charge capture patterns, and support financial reporting that requires gross-to-net reconciliation.
Standard Abbreviation
gross_coll_rt
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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