claim lag days
clm_lag_daysDefinition
ISO-11179 Definition
The number of days between the date of healthcare service delivery and the date the corresponding claim is submitted to the insurance payer, measuring the speed of the charge capture and billing workflow. Claim lag days directly affect cash flow timing and timely filing compliance — excessive lag increases the risk of timely filing denials and delays cash collection. Industry best practice targets claim lag of three to five days for electronic professional claims and five to seven days for facility claims, with same-day submission goals for high-volume routine services.
Factors contributing to claim lag include incomplete clinical documentation requiring physician query before coding, charge capture workflow delays in the electronic health record, coding backlogs, and claim scrubbing holds awaiting additional information. Healthcare data teams calculate clm_lag_days by department, provider, service type, and facility to identify bottlenecks in the charge-to-claim workflow, measure improvement from process changes, and estimate the cash flow impact of reducing average claim lag across high-volume service lines.
Standard Abbreviation
clm_lag_days
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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