cash collection rate
cash_coll_rtDefinition
ISO-11179 Definition
The percentage of billed charges that a healthcare organization actually collects as cash receipts during a specific time period, representing the real-world cash conversion efficiency of the revenue cycle. Cash collection rate differs from net collection rate in that it measures actual cash receipts against gross charges rather than against net collectible revenue after contractual adjustments. Cash collection rate is used in short-term cash flow management and forecasting, helping healthcare organizations project actual cash receipts from current billing activity.
Industry average cash collection rates vary significantly by payer mix and provider type, typically ranging from 20 to 40 percent of gross charges when contractual adjustments are considered. Healthcare data teams calculate cash_coll_rt by period, payer category, and service line to support treasury management cash flow forecasting, evaluate collection trend changes that may signal revenue cycle performance shifts, and model cash receipt projections under different payer mix and volume scenarios.
Standard Abbreviation
cash_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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