An American Hospital Association-informed weighting framework for Oracle Health Real-World Data: validation against...

To develop and validate a weighting framework to improve representativeness of Oracle Health Real-World Data (OHRWD).

We conducted cross-sectional analyses of OHRWD encounters in 2019 and 2022. The primary method (M1) applied design weights based on American Hospital Association (AHA) hospital encounter counts to balance OHRWD encounters across strata of US region, hospital system, bed size, and encounter type (inpatient, emergency department, ambulatory surgery). Comparative methods (M2-M5) used unified structural weights, encounter-type multipliers, iterative proportional fitting, and demographic post-stratification. Weighted OHRWD estimates were validated against the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS), Nationwide Emergency Department Sample (NEDS), and Nationwide Ambulatory Surgery Sample (NASS) for demographics, conditions (low back pain, opioid use disorder (OUD), hypertension, diabetes), and procedures (colonoscopy, appendectomy). Equivalence was assessed using two one-sided tests with ±20% margins; standardized differences were summarized using Cohen's d and h.

M1 produced close alignment between OHRWD and HCUP for age, sex, region, and most race/ethnicity groups, with negligible effect sizes. Low back pain, OUD, and appendectomy prevalences closely matched HCUP benchmarks, whereas hypertension and, in some settings, diabetes and Hispanic ethnicity showed larger deviations. Alternative methods (M2-M5) yielded mixed performance and did not consistently outperform M1.

Encounter-type-specific, structurally informed weighting improved alignment with HCUP national benchmarks, highlighting domains, such as hypertension and race/ethnicity estimates, that require additional calibration.

AHA-based, encounter-type-stratified weighting enables OHRWD to better approximate encounter patterns and could support epidemiologic and health services research using large EHR datasets.