medical coding
med_codingDefinition
ISO-11179 Definition
The process of translating clinical documentation of patient diagnoses, procedures, services, and supplies into standardized alphanumeric codes from established code sets including ICD-10-CM for diagnoses, ICD-10-PCS for inpatient procedures, CPT for outpatient procedures, HCPCS Level II for supplies and drugs, and revenue codes for facility billing. Medical coding is performed by certified medical coders who review clinical documentation and assign the most accurate and complete codes supported by the record. Accurate and complete coding directly affects claim payment amounts, risk adjustment scores, quality measure performance, and clinical data analytics.
Undercoding results in revenue loss and understated disease burden while overcoding creates compliance risk and overpayment liability. Healthcare data teams build coding analytics that measure coder productivity and accuracy, track code distribution shifts over time, identify providers with atypical coding patterns, and support clinical documentation improvement programs that increase coding specificity.
Standard Abbreviation
med_coding
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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