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About

Nicholas Sauder is a rising voice in orthopedic surgery research, with a focused interest in the intersection of advanced technology and value-based care in joint arthroplasty and spine surgery. His work critically examines how robotic assistance and deep learning can improve surgical outcomes and patient recovery. Sauder’s major contributions include demonstrating that robotic-assisted total hip arthroplasty can increase the likelihood of achieving clinically meaningful improvement, as shown in his propensity-matched analysis of over 1,300 procedures. He has also advanced the understanding of value in knee arthroplasty, using time-driven activity-based costing to compare the cost-effectiveness of unicompartmental versus total knee arthroplasty. His early work on deep learning applications in spine surgery has garnered attention, and his comparative studies on recovery times between robotic and manual hip arthroplasty provide actionable insights for surgeons. With over 20 citations across his most-cited papers, Sauder is establishing himself as a key contributor to evidence-based, technology-driven orthopedic care.