member raf score
mbr_raf_scrDefinition
ISO-11179 Definition
The Risk Adjustment Factor score calculated for an individual health plan member representing their predicted healthcare cost relative to the average Medicare beneficiary. RAF scores are calculated by CMS using the CMS-HCC model which combines demographic factors including age, gender, and enrollment status with diagnosis-based hierarchical condition category coefficients derived from submitted ICD-10 codes. A RAF score of 1.0 represents average expected cost, scores above 1.0 indicate higher predicted utilization, and scores below 1.0 indicate lower expected costs.
CMS multiplies the county-level base payment rate by the member RAF score to calculate monthly capitation payments to Medicare Advantage plans. Healthcare data teams store mbr_raf_scr as DECIMAL(10,3) in member risk score tables and use it in care management prioritization, network adequacy planning, and actuarial analysis.
Standard Abbreviation
mbr_raf_scr
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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