Abstract: In early March 2020, Mark joined an interdisciplinary team to launch the Pandemic Response Network. Over the subsequent months, he helped build and launch programs to support health workers, university students and staff, small businesses, K-12 public schools, and historically marginalized communities through the COVID-19 pandemic. With a strong background in design and implementation of high-tech health innovations, Mark worked alongside public health practitioners and community leaders to repeatedly execute the last mile implementation of critical COVID-19 programs, including symptom monitoring in the workplace, rapid antigen testing in schools, and pop-up vaccination events in churches. The portfolio of programs rapidly shifted health care capabilities and expertise out of hospitals and clinics into community settings that were poorly supported by existing public health infrastructure. The experience forced Mark and his team to approach technology design with a new set of assumptions and led to the development of completely novel data streams and technology systems. In his talk, Mark distills insights and learnings from the front lines of the COVID-19 response and highlights important implications and opportunities for the field of machine learning and artificial intelligence in health care.

Bio: Mark Sendak, MD, MPP is the Population Health & Data Science Lead at the Duke Institute for Health Innovation (DIHI), where he leads interdisciplinary teams of data scientists, clinicians, and machine learning experts to build technologies that solve real clinical problems. He has built tools to support Duke Health's Accountable Care Organization, COVID-19 Pandemic Response Network, and hospital network. Together with his team, he has integrated dozens of data-driven technologies into clinical operations and is a co-inventor of software to scale machine learning applications. He leads the DIHI Clinical Research & Innovation scholarship, which equips medical students with the business and data science skills required to lead health care innovation efforts. His work has been published in technical venues such as the Machine Learning for Healthcare Proceedings and Fairness, Accountability, and Transparency in Machine Learning Proceedings and clinical journals such as Plos Medicine, Nature Medicine and JAMA Open. He has served as an expert advisor to the American Medical Association, AARP, and National Academies of Medicine on matters related to machine learning, innovation, and policy. He obtained his MD and Masters of Public Policy at Duke University as a Dean's Tuition Scholar and his Bachelor's of Science in Mathematics from UCLA.