revenue cycle
rev_cycleDefinition
ISO-11179 Definition
The complete administrative and clinical functions that contribute to the capture, management, and collection of patient service revenue in a healthcare organization. The revenue cycle begins when a patient schedules an appointment and ends when all payments for that encounter are collected in full. Key revenue cycle stages include patient registration and eligibility verification, charge capture, medical coding, claim submission, payment posting, denial management, and accounts receivable follow-up.
Efficient revenue cycle management is critical to healthcare organization financial sustainability — even small improvements in clean claim rates or denial resolution can generate millions in recovered revenue annually. Healthcare data teams build revenue cycle analytics pipelines that track key performance indicators including days in accounts receivable, first-pass claim acceptance rates, denial rates by payer and reason code, and net collection rates to identify bottlenecks and measure revenue cycle performance improvements over time.
Standard Abbreviation
rev_cycle
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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