accounts receivable
accts_rcvblDefinition
ISO-11179 Definition
The total amount of money owed to a healthcare organization for services rendered but not yet collected from patients, insurance payers, or government programs. Accounts receivable represents the outstanding revenue that the organization has earned but has not yet received in cash payment. Healthcare accounts receivable is complex due to the involvement of multiple payers with different contract terms, the time lag between service delivery and payment, and the high volume of claim denials and partial payments that require follow-up.
Effective accounts receivable management requires systematic follow-up workflows, denial resolution processes, and patient balance collection programs. Healthcare data teams monitor accts_rcvbl through aging bucket analysis categorizing outstanding balances by days since billing into 0-30, 31-60, 61-90, 91-120, and over 120 day buckets to identify stale receivables at risk of write-off and prioritize collection efforts.
Standard Abbreviation
accts_rcvbl
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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