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culture revision

cult_rev
laboratory·Updated Jun 23, 2026

Definition

ISO-11179 Definition

Tracks the version or iteration number of a microbiology culture order, result, or related clinical documentation that has been updated or amended. Used in laboratory and EHR audit trails to maintain record integrity, identify corrected results, and comply with chain-of-custody requirements for diagnostic specimens.

Standard Abbreviation

cult_rev

Category

laboratory

Production DDL — FACT_LAB_RESULT

FACT_LAB_RESULT.sql
CREATE OR REPLACE TABLE FACT_LAB_RESULT (
    lab_rslt_key    INTEGER        NOT NULL  -- surrogate key,
    mbr_key         INTEGER        NOT NULL  -- FK to DIM_MEMBER,
    prvdr_key       INTEGER        NOT NULL  -- FK to DIM_PROVIDER,
    loinc_cd        VARCHAR(10)              -- LOINC code,
    lab_test_nm     VARCHAR(255)             -- lab test name,
    lab_rslt_val    DECIMAL(18,4)            -- result value,
    lab_rslt_unit   VARCHAR(20)              -- result unit,
    ref_range_low   DECIMAL(18,4)            -- reference range low,
    ref_range_high  DECIMAL(18,4)            -- reference range high,
    abnorm_ind      CHAR(1)                  -- abnormal indicator,
    crit_val_ind    CHAR(1)                  -- critical value flag,
    coll_dt         DATE                     -- collection date,
    rslt_dt         DATE                     -- result date,
    ord_prvdr_npi   VARCHAR(10)              -- ordering provider NPI,
    load_dt         TIMESTAMP_NTZ  NOT NULL  -- load timestamp
);

Standard Snowflake DDL for the canonical laboratory 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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