days in accounts receivable
days_arDefinition
ISO-11179 Definition
A key revenue cycle performance metric measuring the average number of days it takes a healthcare organization to collect payment after services are rendered, calculated by dividing net accounts receivable by average daily net patient service revenue. Days in accounts receivable is one of the most important indicators of revenue cycle efficiency — lower values indicate faster collection and better cash flow management. Industry benchmarks vary by provider type, with physician practices typically targeting under 40 days and hospitals targeting under 50 days.
High days in accounts receivable signals problems in claim submission timeliness, denial management effectiveness, or patient balance collection processes. Healthcare data teams calculate days_ar at the organization, department, payer, and service line levels to identify performance variation, track improvement over time, and benchmark against industry standards from HFMA, MGMA, and Advisory Board surveys.
Standard Abbreviation
days_ar
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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