clinical documentation improvement
cdiDefinition
ISO-11179 Definition
A program within healthcare revenue cycle and quality management that works proactively with clinical providers to ensure medical record documentation accurately, completely, and specifically reflects the patient clinical condition and care delivered, supporting accurate coding, appropriate reimbursement, and valid quality measurement. CDI specialists review inpatient medical records concurrently during hospitalization and query physicians when documentation is unclear, incomplete, or inconsistent with the clinical picture. CDI programs focus on capturing present-on-admission conditions, complications and comorbidities that affect DRG assignment, clinical validation of diagnoses, and specificity of documentation to support accurate ICD-10 code assignment.
Healthcare data teams measure CDI program performance through metrics including query rate, query response rate, query agreement rate, case mix index impact, and estimated revenue impact of documentation improvements to demonstrate CDI program return on investment.
Standard Abbreviation
cdi
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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