first pass resolution rate
fprr_pctDefinition
ISO-11179 Definition
The percentage of submitted healthcare claims that are paid correctly on the first adjudication attempt without requiring any rework including denial management, rebilling, or appeals. First pass resolution rate is a comprehensive measure of revenue cycle quality that captures the combined effect of registration accuracy, eligibility verification, authorization management, coding accuracy, and claim preparation quality. A high first pass resolution rate above 90 percent indicates an efficient revenue cycle that minimizes rework costs and maximizes cash flow velocity.
Low first pass resolution rates generate significant administrative costs from denial management activities and delay cash collections. Healthcare data teams calculate fprr_pct by payer, service line, facility, and provider to identify performance variation, compare against industry benchmarks from HFMA and Advisory Board surveys, measure improvement over time as revenue cycle optimization initiatives are implemented, and quantify the financial return on investments in registration technology, coding education, and claim scrubbing tools.
Standard Abbreviation
fprr_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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