SAT-602 - Barriers in Screening for Metabolic Dysfunction-Associated Steatotic Liver Disease in Adults with Type 2 Diabetes Mellitus

Background: Metabolic dysfunction–associated steatotic liver disease (MASLD) affects approximately 70% of patients with type 2 diabetes (T2DM) and is a leading cause of cirrhosis. Despite guideline recommendations for noninvasive fibrosis risk stratification, MASLD remains under-recognized in routine diabetes care. We developed an endocrine fellow-led quality improvement (QI) initiative that applied formal QI methodology to improve MASLD detection in an academic endocrinology clinic.

Methods: Using the DMAIC (Define–Measure–Analyze–Improve–Control) framework, we evaluated MASLD screening processes for adults with T2DM (ICD-10 E11) seen between January-December 2025 at the endocrinology clinic at Vanderbilt University. The process metric was completion of laboratory data required to calculate a Fibrosis-4 score (FIB-4: AST, ALT, platelet count). We constructed current-state process maps and performed Gemba walks with staff and clinicians.

Results: Current-state mapping found no standardized process for fibrosis risk assessment. MASLD was only considered when transaminases were elevated or when steatosis was incidentally identified on imaging. Initial process measure dashboard review suggested only 1% of patients had complete FIB-4 data; however, direct data validation did not correlate with this. Investigation found that laboratory values embedded in common panels (e.g., AST/ALT from CMP, platelets from CBC) were not captured due to electronic medical record lab code exclusions, revealing a data collection failure which likely underestimates our screening numbers. Inclusion criteria are being corrected, and baseline performance data will be updated. A root cause analysis identified three high-frequency, high-impact barriers: 1) no standardized workflow for lab collection, 2) uncertainty regarding ownership of abnormal labs, and 3) competing preventive care demands leading to screening fatigue.

Intervention: The future-state map and solutions include potentially embedding lab verification into intake processes, enabling automated FIB-4 calculation, and integrating educational decision support tools within the diabetes visit template to guide next steps.

Conclusions: MASLD is an underestimated threat in T2DM that can lead to excess morbidity and significant cost to the medical system. This QI project uncovered multiple systems-level barriers in an academic endocrinology clinic that hinder MASLD screening. Deeper understanding of the metric revealed that inaccuracy in measurements can mask true performance and undermine improvement, highlighting that reliable measurement is the backbone of effective real world QI work. Upcoming PDSA cycles will work towards solution implementation.

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