Abstract: Using Electronic Health Records (EHRs), it is possible to examine the outcomes of decisions made by doctors during routine care and generate evidence from the collective experience of patients. We will discuss our efforts at Stanford Medicine for transforming unstructured EHR data to discover hidden trends, build predictive models, and drive comparative effectiveness studies in a learning health system.
Dr. Nigam H. Shah is an Associate Professor of Medicine at the Stanford University. Dr. Shah’s research focuses on combining machine learning and prior knowledge in medical ontologies to enable use cases of the learning health system. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and is inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.