net collection rate
net_coll_rtDefinition
ISO-11179 Definition
A key revenue cycle performance metric measuring the percentage of net collectible revenue actually collected by a healthcare organization, calculated by dividing collections by net charges after subtracting contractual adjustments. Net collection rate measures how effectively a revenue cycle captures the revenue it is entitled to receive and is considered one of the most important indicators of overall revenue cycle performance. A net collection rate of 96 percent or above is generally considered best practice, meaning the organization collects 96 cents of every dollar it is contractually owed.
Lower net collection rates indicate revenue leakage from uncollected patient balances, unresolved denials, missed filing deadlines, or ineffective collection processes. Healthcare data teams calculate net_coll_rt at the organization, payer, service line, and facility level to benchmark performance against industry standards, identify revenue cycle improvement priorities, and measure the financial impact of revenue cycle optimization initiatives over time.
Standard Abbreviation
net_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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