shared losses
shrd_loss_amtDefinition
ISO-11179 Definition
The financial obligation owed by healthcare providers participating in two-sided risk value based care arrangements when their total cost of care for the attributed population exceeds the established expenditure benchmark by more than the minimum savings or losses threshold. Shared losses represent the downside financial risk that distinguishes advanced two-sided risk tracks from one-sided savings-only arrangements. In MSSP two-sided tracks, ACOs must repay a percentage of losses above the minimum loss rate up to a maximum loss sharing limit.
The decision to accept downside risk is significant for provider organizations and requires sophisticated financial modeling of potential loss scenarios and risk mitigation strategies. Healthcare data teams calculate projected shared losses by measuring the gap between actual and benchmark expenditure, applying the applicable loss sharing rate from the contract, monitoring in-year spending trends to project final settlement amounts, and modeling risk mitigation interventions including care management programs and utilization management initiatives.
Standard Abbreviation
shrd_loss_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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