Stanford University
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Adam J. Murrietta, MD
Clinical Assistant Professor, Orthopaedic Surgery
BioDr. Adam J. Murrietta is a fellowship-trained orthopaedic surgeon with Stanford Health Care. He is also a clinical assistant professor in the Department of Orthopaedic Surgery at Stanford University School of Medicine.
Dr. Murrietta diagnoses and treats a wide range of bone and joint conditions, including arthritis, bursitis, fractures, and infections. As an orthopaedic surgeon, he specializes in joint replacement surgery, with a focus on hip and knee replacements. He has advanced expertise in minimally invasive techniques, robotic-assisted procedures, and outpatient joint replacement. Additionally, Dr. Murrietta has extensive training in the latest surgical technologies and uses these innovations to reduce pain and enhance outcomes for his patients.
Dr. Murrietta’s research focuses on improving surgical outcomes and the treatment and management of orthopaedic injuries. His ongoing work focuses on joint replacement techniques and patient-reported outcomes. His research has been published in peer-reviewed journals, including The Journal of Arthroplasty, The Bone & Joint Journal, and Journal of the American Academy of Orthopaedic Surgeons. He has also presented his findings at regional and national conferences, including annual meetings of the Western Orthopaedic Association and the American Association of Hip and Knee Surgeons.
Dr. Murrietta is a fellow of the American Association of Hip and Knee Surgeons (AAHKS) and the American Academy of Orthopaedic Surgeons (AAOS). He is also a member of the American Medical Association (AMA) and the Western Orthopaedic Association (WOA). -
Mark Musen
Stanford Medicine Professor of Biomedical Informatics Research, Professor of Medicine (Computational Medicine) and of Biomedical Data Science
Current Research and Scholarly InterestsModern science requires that experimental data—and descriptions of the methods used to generate and analyze the data—are available online. Our laboratory studies methods for creating comprehensive, machine-actionable descriptions both of data and of experiments that can be processed by other scientists and by computers. We are also working to "clean up" legacy data and metadata to improve adherence to standards and to facilitate open science broadly.
