📝 SummarXiv

Abstract

This case study details The University of Texas Medical Branch (UTMB)'s partnership with Zauron Labs, Inc. to enhance detection and coding of aortic calcifications (ACs) using chest radiographs. ACs are often underreported despite their significant prognostic value for cardiovascular disease, and UTMB partnered with Zauron to apply its advanced AI tools, including a high-performing image model (AUC = 0.938) and a fine-tuned language model based on Meta's Llama 3.2, to retrospectively analyze imaging and report data. The effort identified 495 patients out of 3,988 unique patients assessed (5,000 total exams) whose reports contained indications of aortic calcifications that were not properly coded for reimbursement (12.4% miscode rate) as well as an additional 84 patients who had aortic calcifications that were missed during initial review (2.1% misdiagnosis rate). Identification of these patients provided UTMB with the potential to impact clinical care for these patients and pursue $314k in missed annual revenue. These findings informed UTMB's decision to adopt Zauron's Guardian Pro software system-wide to ensure accurate, AI-enhanced peer review and coding, improving both patient care and financial solvency. This study is covered under University of Texas Health San Antonio's Institutional Review Board Study ID 00001887.