(167) Optimizing BMI Data Accuracy: Synergy Of Claims And EHR Databases

Background: Body mass index (BMI) is used to classify obesity, an important indicator of health status and a risk factor for numerous chronic diseases.

Objectives: This study evaluates the enhancement of weight availability by integrating electronic health record (EHR)-derived BMI data with existing claims, addressing the limitations of relying solely on administrative codes for BMI information.

Methods: A retrospective study was conducted on adult patients (>18 years) from January 1, 2017 to September 30, 2023. The number of patients with BMI records identified via health insurance claims from the Komodo Research Dataset (KRD) alone using ICD-10-CM codes (Z68.1, Z68.2, Z68.3, Z68.4) was compared with those identified via the same approach and/or EHR from the Komodo Clinical Observations (KCO) using structured clinical values. The frequency of BMI records per patient in both datasets was also analyzed. To assess the quality of BMI obtained from EHR, the average BMI from KCO was compared with that in KRD (median BMI in value range defined in each ICD code description adopted) for patients present in both databases. For patients with multiple BMI records on one day, only one BMI score was considered. Findings were reported for the overall study period and stratified by year and gender.

Results: KRD and KCO together identified 49.1% more patients with at least one BMI record (n=148,178,206) compared with KRD alone (n = 99,400,510). The frequency of BMI observations per person remained similar by supplementing with KCO (5.2 (standard deviation [SD] 8.8) vs KRD alone 5.5 (8.8). Among patients with both KRD and KCO records, their average BMI from EHR (31.5 [7.0]) is similar to that from claims (31.5 [8.7]). Specifically, 78.9% had their EHR-derived BMI value within 2-point range of their ICD-coded BMI value, demonstrating the reliability of EHR-derived BMI. Similar results were observed by year and gender.

Conclusions: Incorporating clinical observation values from EHR with claims-based diagnosis increases the overall identification of patients with valid BMI values and their frequency of BMI records without compromising the quality of BMI reporting.