Ziad Obermeyer

Ziad Obermeyer, MD, MPhil is Associate Professor of Health Policy and Management at the UC Berkeley School of Public Health, jointly appointed at UC San Francisco, where he leads a research program at the intersection of machine learning, clinical medicine, and health policy. Dr. Obermeyer is globally recognized for his landmark 2019 Science paper exposing systematic racial bias in a widely-deployed commercial healthcare algorithm used to allocate care management resources to high-risk patients, demonstrating that the algorithm underestimated the severity of illness in Black patients due to using healthcare costs (which reflect systemic inequities in access) rather than illness burden as the training label. This work catalyzed a major international conversation about algorithmic fairness in medicine and directly led to corrective actions by the algorithm's developer, Optum. Beyond algorithmic bias, Dr. Obermeyer's laboratory develops machine learning models for predicting clinical deterioration, cancer risk, sepsis, and unplanned care utilization from electronic health records and claims data, with a focus on models that are both statistically powerful and deployable at scale. He trained in emergency medicine and health policy at Harvard Medical School before joining Berkeley, and maintains active clinical practice. His work has been published in Science, NEJM, Nature Medicine, and JAMA, and has shaped FDA and CMS guidance on AI in healthcare.