timely filing
tmly_filing_indDefinition
ISO-11179 Definition
A boolean indicator identifying whether a healthcare claim was submitted to the payer within the contractually or regulatorily required timeframe from the date of service. Timely filing requirements are established in provider contracts and payer policies, with deadlines ranging from 90 days for some commercial payers to 12 months for Medicare and up to 24 months for some Medicaid programs from the date of service or discharge. Claims submitted after the timely filing deadline are denied as untimely and typically cannot be appealed unless the provider can demonstrate the delay was caused by circumstances beyond their control such as payer system errors or coordination of benefits delays.
Timely filing denials represent permanent revenue loss that cannot be recovered. Healthcare data teams track tmly_filing_ind in claims submission analytics to identify charge capture and billing workflow delays, measure the financial impact of untimely submissions by department and provider, and implement process controls that flag encounters approaching filing deadlines.
Standard Abbreviation
tmly_filing_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.
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