cost to collect
cst_to_collDefinition
ISO-11179 Definition
A revenue cycle efficiency metric measuring the total administrative cost incurred by a healthcare organization to collect one dollar of net patient service revenue, calculated by dividing total revenue cycle operating expenses by net collections. Cost to collect benchmarks vary by provider type and organization size, with high-performing health systems achieving costs below three cents per dollar collected while average performers may spend five to seven cents per dollar. Higher cost-to-collect ratios indicate revenue cycle inefficiency from excessive manual processes, high denial rates requiring rework, inadequate point-of-service collection, or overstaffed billing departments relative to volume.
Healthcare data teams calculate cst_to_coll by analyzing revenue cycle department staffing costs, technology expenses, collection agency fees, and overhead against net revenue collected, tracking this metric over time to measure the return on revenue cycle technology investments and process improvement initiatives.
Standard Abbreviation
cst_to_coll
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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