collections
collectnsDefinition
ISO-11179 Definition
The systematic process of pursuing payment for outstanding healthcare accounts receivable from insurance payers, patients, or responsible parties through follow-up activities including claim resubmission, appeals, statement generation, payment plan arrangements, and third-party collection agency placement. Effective collections management requires a structured workflow that assigns accounts to collectors based on payer type and balance size, establishes follow-up schedules for each account category, tracks all collection activities in the patient accounting system, and escalates unresolved balances through defined collection stages. Healthcare data teams build collections analytics that measure collector productivity by accounts worked and dollars recovered, track collection rates by payer and balance age, analyze payment pattern trends to optimize follow-up timing, and calculate the return on investment of different collection strategies including internal follow-up versus collection agency placement.
Standard Abbreviation
collectns
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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