member stratification
mbr_stratDefinition
ISO-11179 Definition
The assignment of a health plan member to a risk tier or acuity category based on predicted healthcare cost, clinical complexity, social needs, or care gap burden for the purpose of care management program prioritization and resource allocation. Stratification models segment members into tiers such as high risk, rising risk, moderate risk, and low risk using predictive analytics applied to claims history, pharmacy utilization, clinical assessment scores, and social determinants of health data. High-risk members receive intensive case management while rising-risk members receive targeted disease management outreach.
Healthcare data teams build stratification pipelines that apply predictive risk models to member data, assign tier codes, track tier transitions over time, and feed stratification outputs to care management platforms for outreach queue prioritization and intervention tracking.
Standard Abbreviation
mbr_strat
Category
Production DDL — DIM_MEMBER
CREATE OR REPLACE TABLE DIM_MEMBER (
mbr_key INTEGER NOT NULL -- surrogate key,
mbr_id VARCHAR(50) NOT NULL -- member identifier,
mbr_first_nm VARCHAR(100) -- first name,
mbr_last_nm VARCHAR(100) -- last name,
mbr_birth_dt DATE -- date of birth,
mbr_gndr_cd CHAR(1) -- gender code M/F/U,
mbr_age SMALLINT -- age in years,
mbr_state_cd CHAR(2) -- state code,
mbr_zip_cd VARCHAR(10) -- zip code,
mbr_elig_ind BOOLEAN -- eligibility indicator,
mbr_enrl_dt DATE -- enrollment date,
mbr_term_dt DATE -- termination date,
mbr_plan_cd VARCHAR(20) -- plan code,
mbr_dual_elig_cd VARCHAR(10) -- dual eligibility code,
load_dt TIMESTAMP_NTZ NOT NULL -- load timestamp
);
Standard Snowflake DDL for the canonical member table. Convert to BigQuery or Databricks →
Why This Term Matters
Member and enrollment data governs who receives care and who pays for it — making it foundational to every downstream healthcare analytics workflow. Data engineers who understand member terminology build eligibility pipelines that prevent coverage gaps, correctly identify dual-eligible members, and support accurate risk adjustment submissions to CMS. Enrollment errors directly affect capitation payments and can trigger CMS corrective action plans.
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