Confirmed Keynotes and Invited Talks
Keynotes 01: Professor Kathleen Gray
Title: Changing human roles and responsibilities in the digital transformation of health systems
Abstract: Much of the talk about digital transformation in healthcare heightens our expectations of how it will improve our health systems. However much less is said about how we must also change our expectations of ourselves, our roles and our responsibilities, before such improvements can be realised. Increasingly sophisticated digital technologies - such as artificial intelligence, mobile apps, online social networks, robotics, virtual environments and wearable sensors – give us great ability to reshape the design and delivery of healthcare. On one hand, the way that the potential impact of these technologies is described often makes it seem as if such changes will happen in healthcare automatically, with no human involvement required. On the other hand, we know that healthcare serves the needs of very large numbers of people and is serviced by a very large part of the workforce in most societies, and that many people now are being exposed to new healthcare practices that most have not yet understood or experienced. Research underway at the University of Melbourne highlights some of the new roles and responsibilities becoming available to human actors in health - namely citizens and patients, clinicians and other care providers, teachers and researchers, health service managers and policy makers, and health information professionals – in society’s efforts to ensure that digital health actually improves health system performance. Many examples of innovative role descriptions and newly-defined accountabilities are emerging, including biomedical citizen scientists, e-patients, clinical information officers, health data scientists, clinical simulation educators, health cybersecurity managers, and digital health facilitators.
Bio: Kathleen Gray is an Associate Professor of Health Informatics at The University of Melbourne, jointly appointed in the Melbourne Medical School and the School of Computing and Information Systems. From 2016 to 2019 she has been the Acting Director of its Health and Biomedical Informatics Centre (HaBIC).
She has had a continuing academic position at The University of Melbourne since 2005; initially as a researcher and developer in Educational Technology, in the Biomedical Multimedia Unit, Faculty of Medicine, Dentistry and Health Sciences, and subsequently as a researcher and developer of the University’s health informatics research agenda. From 1986 to 2005, she held a range of teaching, curriculum development, academic and organisational leadership and development positions at RMIT University. From 2001 she directed its educational quality initiatives for Science, Engineering and Technology, and led those disciplines in implementing that University’s online learning strategy.
She has held roles in national and international professional, industry and policy settings, for example: Canada’s national EHealth Conference scientific committee; US Health Information Management and Systems Society conference review committee; International Medical Informatics Association Participatory Informatics Working Group and Exposome Working Group; Chair of the Australasian College of Health Informatics Education Committee; Governance Board Member, Certified Health Informatician Australasia Program; Australian Digital Health Agency Education and Workforce Steering Committee member, scientific program committee chair and member of the annual Australasian Health Informatics and Knowledge Management conference.
Research focus
Gray is recognised for her work in strengthening research methods in the field of health informatics, including conceptual modelling, participatory design research, and frameworks for evaluation of digital health infrastructure and interventions. She has worked collaboratively with clinicians across areas including audiology, endocrinology, gastroenterology, genomics, gerontology, pain management and psychiatry, to improve research into the design, implementation and evaluation of health information technologies which improve shared patient-clinician decision-making. She is currently engaged in innovative research on the potential for increasing public engagement in health and biomedical research through person-generated health data from mobile, wearable and implantable technologies.
Gray is also recognised for strengthening the evidence base about the changing learning and development needs of the health workforce in the era of digital health. For the past decade she has led ground-breaking work addressing the immaturity of university degrees to equip future clinical professionals for digital practice. During this period she has initiated and coordinated postgraduate programs in health informatics and digital health at The University of Melbourne, which have brought together hundreds of people from IT backgrounds and clinical backgrounds. She also initiated the education directory of the Australasian College of Health Informatics, and is now establishing its Academic Roundtable. She is currently the co-leader of an international health information workforce census project that is documenting the convergent evolution of specialised work roles and skills with the rise of digital health.
Keynotes 02: Professor Fei Wang
Title: Machine Learning for Precision Medicine: Promises, Challenges and Opportunities
Abstract: Precision medicine refers to the paradigm of providing the right treatment to the right patient at the right time. With the rapid development of health and computer technologies, more and more health data are becoming readily available nowadays. Machine Learning and AI techniques hold great promises to dig insights from those data that are helpful for precision medicine. In this talk, I will give an overview of the research that my lab has done in recent years on developing machine learning methods for analyzing various health data including Electronic Health Records (EHR), medical images, pharmaceutical research and development data, biomedical literature, physiological signal streams, as well as conversational data. I will also discuss the challenges on this topic and future opportunities.
Bio: Fei Wang is currently an Associate Professor in Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University. He got his PhD from Department of Automation, Tsinghua University in 2008. His major research interest is data mining, machine learning and their applications in health data science. He regularly publishes on top venues of both machine learning and data mining (e.g., ICML, KDD, NeurIPS, etc.) and medicine (e.g., JAMA Internal Medicine and Neurology). His papers have received over 9,000 citations so far with an H-index 50. His (or his students’) papers have won 6 best paper (or nomination) awards at top international conferences on data mining and medical informatics (including ICDM, AMIA TBI and SDM). His team won the championship of the NIPS/Kaggle Challenge on Classification of Clinically Actionable Genetic Mutations in 2017 (https://www.mskcc.org/trending-topics/msk-advances-its-ai-machine-learning-nips-2017) and Parkinson's Progression Markers' Initiative data challenge organized by Michael J. Fox Foundation in 2016 (https://www.michaeljfox.org/publication/university-california-san-francisco-and-weill-cornell-medicine-researchers-named?category=7&id=625). Dr. Wang is the recipient of the NSF CAREER Award in 2018, as well as the inaugural research leadership award in IEEE International Conference on Health Informatics (ICHI) 2019. Dr. Wang is the chair of the Knowledge Discovery and Data Mining working group in American Medical Informatics Association (AMIA). Dr. Wang frequently serves as the program committee chair, general chair and area chair at international conferences on data mining and medical informatics. Dr. Wang is on the editorial board of several prestigious academic journals including Scientific Reports, IEEE Transactions on Neural Networks and Learning Systems, Data Mining and Knowledge Discovery, etc.
