coding accuracy
cd_accry_pctDefinition
ISO-11179 Definition
The percentage of coded healthcare encounters where the assigned diagnosis and procedure codes accurately reflect the clinical documentation with no errors of commission or omission, measured through retrospective coding audits comparing coder assignments against an independent expert review. Coding accuracy is a key quality metric for healthcare revenue integrity and compliance programs. Industry standards target coding accuracy rates above 95 percent for professional coding and above 90 percent for facility coding.
Coding errors include incorrect code assignment, missing secondary diagnoses that would affect DRG assignment or risk adjustment, incorrect procedure code specificity, unsupported codes not documented in the medical record, and sequencing errors that affect principal diagnosis selection. Healthcare data teams track cd_accry_pct by coder, specialty, and error type to identify training needs, measure the financial impact of coding errors on reimbursement and risk adjustment, and demonstrate compliance program effectiveness to regulators and auditors.
Standard Abbreviation
cd_accry_pct
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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