aco benchmark
aco_bnchmark_amtDefinition
ISO-11179 Definition
The risk-adjusted per capita expenditure target established for a specific Accountable Care Organization at the start of a performance year, representing the spending level below which the ACO must perform to generate shared savings. ACO benchmarks are calculated from three years of historical Medicare claims data for the attributed population, adjusted for changes in patient risk scores between the benchmark period and performance year, and updated with national trend factors. Benchmark rebasing occurs periodically to reset the spending target based on more recent data, which can significantly affect ACO financial performance especially for organizations that have successfully reduced costs below their historical baseline.
Healthcare data teams maintain ACO benchmark calculations that track year-over-year benchmark changes, model the impact of risk score fluctuations on benchmark adjustments, calculate projected shared savings at current spending run rates, and identify the cost reduction opportunities needed to achieve savings targets when actual spending trends above the benchmark.
Standard Abbreviation
aco_bnchmark_amt
Category
Production DDL — FACT_QUALITY_MEASURE
CREATE OR REPLACE TABLE FACT_QUALITY_MEASURE (
qlty_key INTEGER NOT NULL -- surrogate key,
mbr_key INTEGER NOT NULL -- FK to DIM_MEMBER,
plan_key INTEGER NOT NULL -- FK to DIM_PLAN,
meas_yr SMALLINT -- measurement year,
hedis_meas_cd VARCHAR(20) -- HEDIS measure code,
denom_ind CHAR(1) -- denominator eligible,
numer_ind CHAR(1) -- numerator met,
excl_ind CHAR(1) -- exclusion indicator,
gap_open_ind CHAR(1) -- care gap open,
star_rtg_nbr DECIMAL(3,1) -- star rating,
qlty_scr DECIMAL(5,2) -- quality score,
perf_thrsh_pct DECIMAL(5,2) -- performance threshold,
raf_scr DECIMAL(10,3) -- risk adjustment factor,
outreach_cnt SMALLINT -- outreach attempts,
load_dt TIMESTAMP_NTZ NOT NULL -- load timestamp
);
Standard Snowflake DDL for the canonical quality table. Convert to BigQuery or Databricks →
Why This Term Matters
Quality measure data determines how payers and providers are rated and reimbursed under CMS Stars, HEDIS, and value-based care contracts. Data engineers who understand quality terminology build measure calculation pipelines that correctly attribute patients, apply denominator exclusions, and flag documentation gaps before submission deadlines. Incorrect quality data directly affects star ratings, pay-for-performance bonuses, and Medicare Advantage plan bids.
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