payer mix
pyr_mixDefinition
ISO-11179 Definition
The distribution of a healthcare organization patient volume and revenue across different insurance payer categories including commercial insurance, Medicare, Medicaid, self-pay, and other government programs. Payer mix is a critical financial planning metric because different payer types reimburse at dramatically different rates — commercial payers typically reimburse at 120 to 160 percent of Medicare rates while Medicaid reimburses at 60 to 80 percent of Medicare for most services. A payer mix shift toward lower-paying government payers or self-pay patients directly reduces net revenue even when patient volumes remain stable.
Healthcare data teams analyze pyr_mix trends over time by facility, service line, and physician to identify shifts that affect financial performance, model the revenue impact of payer mix changes in financial planning, compare payer mix to market benchmarks using state discharge data, and evaluate the profitability of service line expansion or contraction decisions.
Standard Abbreviation
pyr_mix
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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