This proposal evaluates the implementation of a novel, non-interruptive, electronic health record alert for metabolic dysfunction-associated steatotic liver disease (MASLD) fibrosis risk assessment in primary care patients with MASLD using a stepped wedge, cluster randomized design. We will evaluate the clinical outcome of advanced liver fibrosis detection and the implementation outcomes of adoption, penetration, fidelity, and sustainability. This work will generate generalizable data to dramatically enhance MASLD management in primary care.

To improve the diagnosis of metabolic dysfunction-steatotic liver disease (MASLD) in primary care, this study will develop, test, and internally validate a predictive model for MASLD in a cross-sectional sample of patients with no known chronic liver disease. Patient metabolic variables, like weight, blood pressure, and blood sugar will be considered for inclusion in the model, and ultrasound-based vibration-controlled elastography will be used for determining the outcome. This work will dramatically enhance MASLD diagnosis and management in primary care.