We are pleased to announce our keynote speakers for ICHI 2017. We think this remarkable set of speakers will inspire you in your respective work.
JoelSaltz served at Emory since 2008 as founding Chair of the Department of Biomedical Informatics, and Professor in the School of Medicine, Department of Pathology and Laboratory Medicine, the College of Arts and Sciences, Department of Mathematics and Computer Science, and the School of Public Health, Department of Biostatistics and Bioinformatics. He led the Biomedical Informatics PhD Track in Emory’s Computer Science and Informatics Program and ran “Clinical and Translational Informatics Rounds” – monthly lectures and discussions in the area of clinical and translational informatics. At Emory, he helped launch Biomedical Informatics-specific Masters and Doctoral programs, in addition to a myriad of other department specific courses on informatics.
Prior to his appointment at Emory, he served as Professor and Founding Chair of the new Department of Biomedical Informatics at The Ohio State University College of Medicine from 2001 to 2008. At Ohio State, he served as Associate Vice President for Health Sciences for Informatics, and he played important leadership roles in the Cancer Center, Heart Institute and Department of Pathology.
Joel Saltz received his Bachelors and Masters of Science degrees in Mathematics at the University of Michigan and then entered the MD/PhD program at Duke University, with his PhD studies performed in the Department of Computer Sciences. He began his academic career in Computer Science at Yale, the Institute for Computer Applications in Science and Engineering at NASA Langley and the University of Maryland College Park. He completed his residency in Clinical Pathology at Johns Hopkins School of Medicine and served as Professor with a dual appointment at the University of Maryland and Johns Hopkins, serving in the University of Maryland Department of Computer Science and Institute for Advanced Computer Studies, and the Johns Hopkins Department of Pathology. Dr. Saltz is a fellow of the American College of Medical Informatics.
Keynote Talk Title: TBA
Jonathan Nebeker, MD, is Deputy Chief Medical Informatics Officer at Veterans Health Administration and Professor of Medicine at the University of Utah. His degrees and training took place at Harvard and the University of Pennsylvania. He is medical director of VA’s program to modernize its health IT systems, is a leader in organizations to advance more robust interoperability, and is the functional architect for VA’s platform to support Veteran-centric, team-based, quality-driven models of healthcare delivery. He pursues research in the epidemiology, EHR user interface design, and analytical IT architecture and methods. He continues to practice geriatrics in Salt Lake City.
Keynote Talk Title: Why the Revolution in High-Quality Healthcare Management Depends on Seamless CareThe quadruple aim from the Institute for Healthcare Improvement summarizes components of healthcare value: patient experience, staff experience, efficiency, and population health. Seamless care is the experience patients and providers have moving from task to task and encounter to encounter within or between organizations such that high-quality decisions form easily and complete care plans execute smoothly. Information systems support the seamless-care experience by gathering data, interpreting data, presenting information, and managing tasks. These are the same functions that require optimization through computer science and informatics methods to realize the triple aim. A focused set of standards and approaches are required for seamless care. This presentation will outline these necessary components of seamless care and how they will drive the revolution in high-quality healthcare.
Jianying Hu, Ph.D.
Jianying Hu is Program Director of Center for Computational Health and a Distinguished Research Staff Member at IBM T.J. Watson Research Center, NY. Prior to joining IBM in 2003 she was with Bell Labs at Murray Hill, New Jersey. Dr. Hu has conducted and led extensive research in machine learning, data mining, statistical pattern recognition, and signal processing, with applications to healthcare analytics and medical informatics, business analytics, document analysis, and multimedia content analysis. Her recent focus has been on leading research efforts to develop advanced machine learning, data mining and visual analytics methodologies for deriving data-driven insights from real world healthcare data to facilitate learning health systems.
Dr. Hu served as Chair of the Knowledge Discovery and Data Mining (KDDM) Working Group of the American Medical Informatics Association (AMIA) from 2014 to 2016. She has published over 120 peer reviewed scientific papers and holds 31 patents. She has served as Associate Editor for the journals IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, Pattern Recognition, and International Journal for Document Analysis and Recognition, and is currently on the advisory board of Journal of Healthcare Informatics Research. Dr. Hu is a fellow of IEEE (elected in 2015), a fellow of the International Association of Pattern Recognition (elected in 2010), and a recipient of the Asian American Engineer of the Year Award (2013).
Keynote Talk Title: Computational Methods for Next Generation Healthcare
Next generation healthcare will be driven by prevention and treatment strategies that take individual variability into consideration. Much of this variability is captured in the large amount of data of different types that has become available: clinical encounters, lab results, diagnostics, medications, genomics, and increasingly, physiological, lifestyle, social behavioral and environmental data. The challenge is how to leverage modern methodologies from machine learning, data mining, visual analytics and decision science, to extract insights from all this data collected over large populations, in order to apply them at patient level to improve outcomes for health and wellness. The overarching goal of Computational Health Research is to enable this journey from complex and diverse health data to useful insights for individuals. At the Center for Computational Health at IBM Research we have been systematically developing advanced data science methodologies for healthcare, ranging from intelligent data preparation and pattern extraction, to complex models for insights generation, to behavioral analysis for personalized interventions. These methodologies have been applied to a wide range of use cases in personalized care delivery and care management, care pathway analytics and practiced based evidence, risk prediction and disease progression modeling, real world evidence for drug discovery, and patient and user engagement. I will discuss these methodologies, use cases, lessons learned and important future directions.