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department end date

dept_end_dt
operations·Updated Jun 23, 2026

Definition

ISO-11179 Definition

The date on which an organizational unit, such as a clinical service line, cost center, or administrative department, ceases to be active within EHR, claims adjudication, or provider directory systems. Used in workforce management and facility configuration tables to close billing hierarchies.

Standard Abbreviation

dept_end_dt

Category

operations

Production DDL — FACT_OPERATIONAL_EVENT

FACT_OPERATIONAL_EVENT.sql
CREATE OR REPLACE TABLE FACT_OPERATIONAL_EVENT (
    oprtnl_evnt_key  INTEGER       NOT NULL  -- surrogate key,
    evnt_id          VARCHAR(50)   NOT NULL  -- source event identifier,
    evnt_type_cd     VARCHAR(50)   NOT NULL  -- event type code,
    evnt_dt          DATE          NOT NULL  -- event date,
    evnt_ts          TIMESTAMP               -- event timestamp,
    fac_id           VARCHAR(20)             -- facility identifier,
    dept_cd          VARCHAR(20)             -- department code,
    unit_cd          VARCHAR(20)             -- unit code,
    mbr_id           VARCHAR(20)             -- member identifier,
    prvdr_npi        VARCHAR(10)             -- provider NPI,
    evnt_sts_cd      VARCHAR(20)             -- event status code,
    evnt_sts_dt      DATE                    -- status date,
    capacity_cnt     INTEGER                 -- capacity count,
    utilztn_pct      DECIMAL(5,2)            -- utilization percentage,
    rec_creat_dt     TIMESTAMP     NOT NULL  -- record created date,
    rec_updt_dt      TIMESTAMP     NOT NULL  -- record updated date
);

Standard Snowflake DDL for the canonical operations 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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