collection agency
coll_agcyDefinition
ISO-11179 Definition
A third-party company contracted by a healthcare organization to pursue collection of patient account balances that remain unpaid after the provider internal collection efforts have been exhausted, typically receiving a contingency fee percentage of amounts collected. Collection agency placement typically occurs after 90 to 120 days of failed internal collection attempts and before the account is written off to bad debt. The Fair Debt Collection Practices Act governs collection agency conduct and restricts collection methods to protect consumers.
Healthcare organizations must comply with HIPAA when sharing patient information with collection agencies and are required to screen collection agency placements against financial assistance policy eligibility before placement. Healthcare data teams track coll_agcy performance metrics including placement rates, liquidation rates comparing amounts collected to amounts placed, and average days to collection to evaluate agency effectiveness and optimize placement timing and agency selection.
Standard Abbreviation
coll_agcy
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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