write off
acct_wo_amtDefinition
ISO-11179 Definition
The formal accounting process of removing an uncollectible patient or payer balance from a healthcare organization accounts receivable after all reasonable collection efforts have been exhausted. Write-offs occur in two primary categories — contractual write-offs representing the difference between a provider billed charges and the payer contracted allowed amount which is routinely adjusted as part of normal claims processing, and bad debt write-offs representing patient balances that remain uncollected after collection efforts including statements, collection calls, and third-party collection agency placement. Healthcare organizations must establish and follow written financial assistance and collection policies governing when patient balances are written off to bad debt versus charity care.
Healthcare data teams track wo_amt by write-off category, payer, service line, and time period to measure bad debt trends, evaluate collection agency performance, assess financial assistance program utilization, and project write-off reserves for financial statement reporting.
Standard Abbreviation
acct_wo_amt
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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