Obesity is a chronic disease with significant health risks but remains underdiagnosed in clinical settings. This study developed and validated a clinical process measure to identify patients with obesity, defined by body mass index (BMI), who lack a formal diagnosis in their electronic health record (EHR) problem list. The measure was pilot-tested across six medical groups (124 clinics, 3,483 providers). Reliability was assessed at the clinic and provider levels using the Beta-binomial model. Predictive validity was evaluated by comparing one-year weight change in patients with and without an obesity diagnosis. Across six participating medical groups, a total of 295,372 eligible patients were identified between January 1 and December 31, 2022. Obesity diagnosis rates ranged from 37.6% to 50.8%. In a predictive cohort of 1,132 patients with at least one follow-up weight recorded within the year following the index visit, those with a documented obesity diagnosis lost an average of 0.34 pounds, while those without gained 1.78 pounds (p=0.026). Reliability was high (clinic-level: 0.97; provider-level: 0.89), demonstrating consistent performance. Findings highlight the clinical importance of documenting obesity in the EHR. Patients with an obesity diagnosis were more likely to lose weight, supporting the measure's predictive validity. This measure addresses gaps in obesity care, promoting early identification, targeted interventions, and better outcomes. Its feasibility as an EHR-based measure enhances adaptability across healthcare settings. Implementing this measure can facilitate systematic obesity management, align with public health priorities, and improve patient care by integrating obesity recognition into routine clinical practice.
Mahon et al. (2025) studied this question.