Metabolic dysfunction-associated steatotic liver disease (MASLD) is a public health crisis affecting one third of the US adult population. Steatotic liver disease is an obesity related disease with serious health and quality of life consequences. Hepatic steatosis is reported on \~10% of written radiology reports. Unfortunately, lack of awareness often leads to hepatic steatosis being overlooked by providers and unreported to patients leading to delays in diagnosis and referral to specialty care.
This project, Improving Diagnostic Safety through STeatosis Identification, Risk stratification, and Referral pathway in the ED (STIRRED), proposes to improve diagnostic safety by ensuring that patients receive timely notification of the new finding and referral to follow up care. The STIRRED CDSS intervention employs natural language processing and machine learning to identify hepatic steatosis in written emergency department (ED) radiology reports and combine it with common clinical data in the electronic health record to create an individualized risk profile and follow up recommendations for patients with suspected MASLD. This recommendation is communicated to the ED clinician during the ED discharge process via an OurPractice Advisory (OPA) and triggering outpatient referral for follow-up care stratified by risk level.
This trial will evaluate STIRRED across a large health system using a type 2 implementation-effectiveness stepped wedge cluster randomized trial across 11 Emergency Departments in a single health system. Effectiveness of providing risk-based care linkage and implementation outcomes will be evaluated.
