unbundling
unbndl_indDefinition
ISO-11179 Definition
A fraudulent or improper billing practice in which a healthcare provider submits multiple separate procedure codes for components of a service that should be billed together under a single comprehensive code, resulting in higher reimbursement than the correct single code would generate. CMS and the AMA National Correct Coding Initiative publish bundling edits that define which procedure code combinations must be billed together as a comprehensive service rather than separately as component services. Unbundling is a leading category of healthcare billing fraud investigated by the OIG and DOJ and can result in False Claims Act liability, civil monetary penalties, and program exclusion.
Healthcare data teams implement unbundling detection analytics that apply NCCI edit tables to claims data, flag procedure code combinations that violate bundling rules, calculate the financial exposure from potentially improper unbundled billing, and generate compliance audit work lists for clinical documentation review.
Standard Abbreviation
unbndl_ind
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.
Related Content
Looking for more healthcare terms?
Browse our complete library of 100,000+ standardized healthcare data terms
Browse All Terms