Estimated 88 percent accuracy seen using electronic health record data, with ability to identify patients not previously diagnosed<\/p>\n<\/h3>\n
By Lori Solomon HealthDay Reporter<\/b>
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WEDNESDAY, Nov. 27, 2024 (HealthDay News) — An algorithm can identify individuals with metabolic dysfunction-associated steatotic liver disease (MASLD) with about 88 percent accuracy using electronic medical record (EMR) data, according to a study presented at The Liver Meeting, the annual meeting of the American Association for the Study of Liver Diseases, held from Nov. 15 to 19 in San Diego.<\/p>\n
Ariana Stuart, M.D., from the University of Washington in Seattle, and colleagues described the development of an EMR-based artificial intelligence (AI) algorithm for MASLD identification. <\/p>\n
The researchers reported that an iterative natural language processing pattern-recognition AI algorithm achieved about 88 percent accuracy versus manual MASLD diagnosis. Among 957 individuals identified by the algorithm as meeting criteria for MASLD, 56 percent were female, 2 percent American Indian\/Native Alaskan, 6 percent Black\/African American, 16 percent Asian, 68 percent White, and 12.2 percent Hispanic\/Latino(a). More than one-fourth of patients (25.6 percent) had gastrointestinal or hepatology referral, but only 140 had an MASLD-associated diagnostic code. More than seven in 10 individuals (697) were identified as meeting criteria for MASLD diagnosis but did not already carry an MASLD diagnosis.<\/p>\n
“People should not interpret our findings as a lack of primary care training or management,” Stuart said in a statement. “Instead, our study shows how AI can complement physician workflow to address the limitations of traditional clinical practice.”<\/p>\n