School of Medicine
Showing 1-10 of 17 Results
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Deeksha Suresh Bidare
Affiliate, Department Funds
Fellow in Peds/Clinical InformaticsBioDr. Deeksha S. Bidare is a general surgery resident physician at Stanford Healthcare, and she has completed three years of clinical training. She is currently in the Professional Development years of her residency training, during which she will complete an ACGME fellowship in Clinical Informatics within the Stanford Department of Pediatrics. Her clinical focus within the Department of Surgery is on Acute Care Surgery, Trauma, and Surgical Critical Care in which she intends to pursue fellowship training after residency. Her research interests within the Clinical Informatics realm include the utilization of EHR and LLM tools to facilitate patient data collection and analysis both in and outside of the operating room.
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Zachary Butzin-Dozier
Assistant Professor of Pediatrics (Clinical Informatics) and of Medicine (Computational Medicine)
BioZach Butzin-Dozier is an Assistant Professor in the Department of Pediatrics, Division of Clinical Informatics, with a joint appointment in the Department of Medicine, Division of Computational Medicine. His research applies machine learning and artificial intelligence for causal inference via electronic health record data. He draws from large-scale databases, such as Epic Cosmos, PEDSnet, and the National Clinical Cohort Collaborative, to answer pressing questions in pediatric and infectious disease medicine. His research evaluates vaccine effectiveness, drug repurposing, and the long-term sequelae of viral infection, including Long COVID. He aims to bridge rigorous biostatistical methodology with clinically meaningful research questions. He received his PhD in Epidemiology and MPH from UC Berkeley, and he is an NIAID K01 recipient.
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Danielle Luz, MD
Affiliate, Department Funds
Fellow in Peds/Clinical InformaticsBioShe is passionate about utilizing health technology to improve the diagnostic odyssey for patients with rare and complex diseases. Her core focus areas include EHR workflow optimization, empowering providers on how to safely utilize AI to generate patient materials to bridge gaps in health literacy, and establishing precision guardrails for the ethical use of AI in genomic medicine.
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Natalie Pageler
Clinical Professor, Clinical Informatics
Clinical Professor, Computational MedicineCurrent Research and Scholarly InterestsIn my administrative role, I oversee the development and maintenance of clinical decision support tools within the electronic medical record. These clinical decision support tools are designed to enhance patient safety, efficiency, and quality of care. My research focuses on rigorously evaluating--1) how these tools affect clinician knowledge, attitudes, and behaviors; and 2) how these tools affect clinical outcomes and efficiency of health care delivery.
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Naveed Rabbani
Adjunct Clinical Assistant Professor, Clinical Informatics
BioDr. Naveed Rabbani is a physician executive in health information technology and medical AI researcher. He currently serves as Associate Chief Medical Information Officer at Sutter Health, a large nonprofit health system in California. In this role, he leads a portfolio of enterprise-wide clinical IT programs including ambient documentation and generative AI implementation. He also holds a research appointment at Harvard Medical School in the Department of Biomedical Informatics. A nationally recognized expert in health information technology, Dr. Rabbani is an Executive Committee Member for the American Academy of Pediatrics' Council on Clinical Information Technology and the Epic EHR Pediatric Primary Care Steering Board. As adjunct faculty at Stanford, he teaches in the Clinical Informatics Fellowship in the School of Medicine and conducts research in the Division of Clinical Informatics. Dr. Rabbani holds a BS in Electrical Engineering from Stanford University and an MD from Harvard Medical School.
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Austin Schoeffler
Affiliate, Department Funds
Fellow in Peds/Clinical InformaticsBioAustin Schoeffler, M.D., is an emergency medicine physician and clinical informatics fellow at Stanford University. Dr. Schoeffler earned his M.D. from The Ohio State University College of Medicine and completed his Emergency Medicine Residency at University Hospitals/Case Western Reserve University in Cleveland. He is currently pursuing a two-year fellowship in Clinical Informatics at Stanford, focusing on the integration of machine learning and digital health solutions within emergency care.
Dr. Schoeffler has a strong background in both clinical operations and digital innovation. He has assisted on projects leveraging AI-driven facial recognition software for depression screening in the emergency department, and is currently critically evaluating the impact of ambient AI scribes on clinical care and helping to create the first AI benchmark for emergency medicine. His operational experience includes governance and workflow optimization at his previous institution, where he contributed to initiatives enhancing patient care delivery and hospital efficiency.
His scholarly interests center on responsible AI integration, innovation, building the future of digital health technology, and expanding access to populations not traditionally reached by existing clinical infrastructure. He is committed to fostering industry-academic partnerships, rigorously evaluating emerging AI tools, and benchmarking AI products for deployment in acute care settings. Clinically, he is passionate about evidence-based care, digital health, and the development of novel care delivery models in emergency medicine. -
Bobak Seddighzadeh
Affiliate, Department Funds
Fellow in Peds/Clinical InformaticsBioOver the past 13 years, Dr. Seddighzadeh has advanced biomedical innovation at Harvard, Stanford, and the Mayo Clinic, integrating emerging technologies with clinical medicine to improve patient care.
Dr. Seddighzadeh’s expertise spans genomic medicine, clinical informatics, and clinical AI. He has built enterprise-level clinical decision support systems that improve care at scale, and as part of the Stanford GUIDE-AI group and the Nigam Shah Lab, he focuses on developing AI-enabled clinical platforms for Stanford’s hospitals and clinics. His work in clinical AI includes implementation, evaluation, and safety guardrails. He also contributes to precision medicine efforts that use multi-omic data to identify disease subtypes and enable more individualized care. As part of Chan Zuckerberg Biohub, he helped build one of the world’s first complete human cell atlases.
In clinical practice, Dr. Seddighzadeh is committed to delivering outstanding internal medicine care to hospitalized patients. He approaches medicine as a craft, continually sharpening diagnostic reasoning and therapeutic decision-making in service of the best possible outcomes. He also values prevention and partners with patients to build sustainable habits that support long-term health and health span.
At New York University, Dr. Seddighzadeh received the Degree Representative Award, an honor conferred by the faculty recognizing the single graduating student with the highest overall academic achievement. He later earned a full-tuition scholarship from the founding dean to attend the University of Nevada, where he graduated with top honors in medicine. He went on to complete his internal medicine residency at Mayo Clinic where he was selected for the Resident Leadership Academy, a specialized program for residents identified across the Mayo Clinic enterprise as future leaders. There he also developed and launched the AI and Medicine Residency Track. He is currently a Clinical Informatics Fellow and internal medicine hospitalist at Stanford University.
