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auto adjudication rate

auto_adj_rt
finance·Updated Jul 21, 2026

Definition

ISO-11179 Definition

The percentage of healthcare claims that are processed and paid or denied automatically through rules-based adjudication systems without requiring manual review by a claims examiner, used as a primary operational efficiency metric for health plan claims operations. High auto-adjudication rates of 90% or above reduce per-claim processing costs and improve payment timeliness, while low rates indicate complex claim mixes, system configuration issues, or high rates of claims requiring clinical review that increase labor costs.

Standard Abbreviation

auto_adj_rt

Category

finance

Production DDL — FACT_CLAIM_TRANSACTION

FACT_CLAIM_TRANSACTION.sql
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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