The Journal of clinical endocrinology and metabolism · 2026
Development and Validation of a Measure Identifying Patients With Obesity by BMI Without an Official Diagnosis.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Mahon Lily Rowan, Bianco Joseph, Xiong Ma, Rothberg Amy
Аннотация
BACKGROUND: Obesity is a chronic disease with significant health risks that remains underdiagnosed in clinical settings. This study developed and validated a clinical process measure to identify patients with obesity, defined by body mass index, who lack a formal diagnosis in their electronic health record (EHR) problem list.
METHODS: The measure was pilot-tested across 6 medical groups (124 clinics, 3483 providers). Reliability was assessed at the clinic and provider levels using the β-binomial model. Predictive validity was evaluated by comparing 1-year weight change in patients with and without an obesity diagnosis.
RESULTS: Across 6 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 1132 patients with at least 1 follow-up weight recorded within the year following the index visit, those with a documented obesity diagnosis lost an average of 0.34 pounds, whereas those without gained 1.78 pounds (P = .026). Reliability was high (clinic-level: 0.97; provider-level: 0.89), demonstrating consistent performance.
DISCUSSION: 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.
CONCLUSION: 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.
METHODS: The measure was pilot-tested across 6 medical groups (124 clinics, 3483 providers). Reliability was assessed at the clinic and provider levels using the β-binomial model. Predictive validity was evaluated by comparing 1-year weight change in patients with and without an obesity diagnosis.
RESULTS: Across 6 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 1132 patients with at least 1 follow-up weight recorded within the year following the index visit, those with a documented obesity diagnosis lost an average of 0.34 pounds, whereas those without gained 1.78 pounds (P = .026). Reliability was high (clinic-level: 0.97; provider-level: 0.89), demonstrating consistent performance.
DISCUSSION: 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.
CONCLUSION: 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.