query rate
qry_rtDefinition
ISO-11179 Definition
The percentage of reviewed inpatient cases in which a clinical documentation improvement specialist issues a physician query requesting clarification, additional specificity, or additional documentation to support accurate and complete medical record coding. Query rate is a key CDI program performance metric that reflects the frequency of documentation opportunities identified during concurrent record review. Appropriate query rates vary by specialty, facility type, and CDI program maturity, typically ranging from 15 to 35 percent of reviewed cases in established programs.
Very high query rates may indicate insufficient clinical documentation habits among medical staff while very low rates may suggest CDI specialists are not identifying all documentation opportunities. Healthcare data teams calculate qry_rt by CDI specialist, physician, service line, and diagnosis category to evaluate CDI program effectiveness, identify physician documentation education priorities, and measure query rate trends over time as documentation improvement initiatives take effect.
Standard Abbreviation
qry_rt
Category
Production DDL — FACT_CLAIM_TRANSACTION
CREATE OR REPLACE TABLE FACT_CLAIM_TRANSACTION (
clm_txn_key INTEGER NOT NULL -- surrogate key,
clm_id VARCHAR(50) NOT NULL -- claim identifier,
mbr_key INTEGER NOT NULL -- FK to DIM_MEMBER,
prvdr_key INTEGER NOT NULL -- FK to DIM_PROVIDER,
clm_typ_cd VARCHAR(10) -- claim type code,
tot_chrg_amt DECIMAL(18,2) -- total charged amount,
tot_alwd_amt DECIMAL(18,2) -- total allowed amount,
tot_pd_amt DECIMAL(18,2) -- total paid amount,
cntrct_adj_amt DECIMAL(18,2) -- contractual adjustment,
denial_ind CHAR(1) -- denial indicator,
denial_rsn_cd VARCHAR(10) -- denial reason code,
prior_auth_nbr VARCHAR(30) -- authorization number,
clm_lag_days SMALLINT -- claim lag days,
days_ar SMALLINT -- days in AR,
load_dt TIMESTAMP_NTZ NOT NULL -- load timestamp
);
Standard Snowflake DDL for the canonical finance 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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