Machine Learning in Public Health: are we there yet?

Jessica Tenenbaum / North Carolina Department of Health and Human Services; Duke University School of Medicine

Abstract: Spoiler alert: No. And yes, it is much, much further. Public health has not traditionally been a data-driven field. The good news is that has been changing in recent years, accelerated significantly by the COVID epidemic. But public health and human services organizations have many more fundamental things to worry about before we will have the luxury of considering what machine learning can enable. These fundamentals include data-related facets such as electronic data capture and exchange, data quality, data governance, information technology infrastructure, and data management best practices. In addition, data literacy, workforce development, and compensation that is a fraction of what 'quants' can earn in industry are also major stumbling blocks toward advanced analytics in public health. At the start of the COVID pandemic, many communicable diseases were reporting by fax machine and then hand-entered into a database. Although there was significant interest in predictive modeling to project hospital capacity out in the future, even the most sophisticated models were of limited use to policy makers beyond basic trends and observations from the front lines. The most notable exception, where AI is in fact proving useful in public health, is in the use of 'robotic process automation' (RPA) as a band-aid for poorly designed systems that require mindless human intervention. These tools serve as workarounds for systems that lack interoperability by emulating human users to do the grunt work of data entry and wrangling. This talk will be a reality check from the trenches of state government on the heels of the COVID-19 pandemic.

Bio: Dr. Tenenbaum serves as the Chief Data Officer (CDO) for DHHS, where she oversees data strategy across the Department enabling the use of information to inform and evaluate policy and improve the health and well-being of residents of North Carolina. Prior to taking on the role of CDO, Dr. Tenenbaum was a founding faculty member of the Division of Translational Biomedical Informatics within Duke University's Department of Biostatistics and Bioinformatics where her research focused on informatics methods to enable precision medicine, particularly in mental health. She is also interested in ethical, legal, and social issues around big data and precision medicine. Nationally, Dr. Tenenbaum has served as Associate Editor for the Journal of Biomedical Informatics and as an elected member of the Board of Directors for the American Medical Informatics Association (AMIA). She currently serves on the Board of Scientific Counselors for the National Library of Medicine. After earning her bachelor's degree in biology from Harvard, Dr. Tenenbaum was a Program Manager at Microsoft Corporation in Redmond, WA for six years before pursuing a PhD in biomedical informatics at Stanford University. Dr. Tenenbaum is a strong promoter and advocate of young women interested in STEM (science, technology, engineering, and math) careers.