Back to 2019 MSM Agenda
Multi-scale modeling has tremendous potential to transform and enhance decision making across many different arenas. Most important decisions in health and healthcare, whether made by product developers and manufacturers, health professionals, funders, policy makers, or other key stakeholders, do not fall exclusively into a single scale. Instead, decisions often must cross different scales and take into acclunt systems ranging from biological ones to population ones and many scales in between. Modeling can help better understand these different scales and systems and thus improve decision making.
However, to fully realize the potential of multi-scale modeling, the key will be finding ways to effectively translate such approaches, methods, tools, and research from research world to the "real world." This is not a trvial process and requires understanding and overcoming a variety of challenges. This session will delve into some of the issues involved and how best to approach them. It will begin with a keynote adress followed by a moderated panel and interactive discussion involving those from different perpectives along the translation spectrum.
Keynote Speaker Bio:
Dr. Charles A. Taylor, Ph.D. co-founded HeartFlow, Inc. and serves as its Chief Technology Officer and Director. Dr. Taylor was an Associate Professor in the Department of Bioengineering and Surgery at Stanford University with courtesy faculty appointments in the Departments of Mechanical Engineering and Radiology. He is internationally recognized for the development of computer modeling and imaging techniques for cardiovascular disease research, device design and treatment planning. He received his B.S. degree in Mechanical Engineering, M.S. degree in Mechanical Engineering and M.S. degree in Mathematics from Rensselaer Polytechnic Institute and a Ph.D. in Mechanical Engineering from Stanford University
Rachel B. Slayton, PhD, MPH, is the lead of the Mathematical Modeling Unit in the Centers for Disease Control and Prevention’s Division of Healthcare Quality Promotion. Dr. Slayton is also the Scientific Director of the Modeling Infectious Diseases in Healthcare (MInD-Healthcare) network. She is interested in developing and utilizing mathematical models and simulation tools to assist in timely public health decision making. She holds a B.S. in cell and molecular biology from Tulane University, and M.P.H. and Ph.D. degrees in epidemiology from the University of Pittsburgh. Dr. Slayton joined CDC in 2011 as an Epidemic Intelligence Service Officer in the Division of Foodborne, Waterborne, and Environmental Diseases. She has co-authored over 60 peer-reviewed journal articles and has received numerous awards for her work including 3 CDC Director’s Honor Awards for Excellence in Quantitative Sciences. Her group’s work focuses on developing mathematical models to better understand the transmission and prevention of multidrug resistant organisms and healthcare associated infections.
Bruce Y. Lee, MD MBA is an Associate Professor of International Health at the Johns Hopkins Bloomberg School of Public Health, Executive Director of the Global Obesity Prevention Center (GOPC) at Johns Hopkins, and Associate Professor at the Johns Hopkins Carey Business School. Dr. Lee has two decades of experience in industry and academia in systems science, digital health, and developing and implementing mathematical and computational methods, models, and tools to assist decision making in public health, health, and medicine. His previous positions include serving as Senior Manager at Quintiles Transnational, working in biotechnology equity research at Montgomery Securities, a co-founder of Integrigen, and serving as an Associate Professor at the University of Pittsburgh. Dr. Lee has authored over 200 scientific publications (including over 100 first author and over 65 last author) as well as three books. Dr. Lee is a regular contributor to Forbes and the Huffington Post and has also written for a range of other general media including Time, The Guardian, and the MIT Technology Review. His work and expertise have appeared in leading media outlets such as the New York Times, USA Today, the Los Angeles Times, Newsweek, CBS News, Businessweek, U.S. News and World Report, Bloomberg News, Reuters, and National Public Radio (NPR). His Twitter handle is @bruce_y_lee
Sarah Buzogany, M.S. is the Food Resilience Planner for the Baltimore Food Policy Initiative. As part of an interagency team in city government, Sarah provides direct support to the Food Policy Director and applies her policy analysis and development skills to a wide range of topics including food environment mapping; food system resilience and emergency response; economic development; urban agriculture; farmers markets and CSAs; food recovery; and more. Previously, Sarah has worked on sustainable agriculture policy at the state and national levels, managed a farmers market, and researched innovative farmer to consumer and nutrition education models. Sarah earned a Master’s in Food Policy and Applied Nutrition from Tufts University Friedman School of Nutrition Science and Policy and holds Bachelor’s degrees in Sustainable Agriculture and Spanish from the University of Kentucky.
Emily Stets is the Project Coordinator for the Sports and Society Program at the Aspen Insitute. She oversees the groundbreaking Project Play 2020 initiative dedicated to raising youth sport participation rates and related metrics. She also assists with the annual Project Play Summit, the nation’s premier gathering of youth, sport and health. In her prior work, Emily developed youth education and advocacy programming in infectious diseases, harm reduction and drug policy in Seattle and Washington, D.C. She solidified her focus on public health prevention models in youth development and collective impact at Search Institute and The Forum for Youth Investment. A proud Minnesotan-turned-D.C.-transplant, Emily graduated from St. Olaf College with a self-designed major, Public Mental Health: Wellness and the Arts, which explored the intersection of mental health, theater, creative writing and psychology. As a former multisport athlete, she believes in the power of equitable access to sport sampling to help young people develop physical literacy and social/emotional skills.
M. Mitchell Waldrop, Ph.D. is a freelance writer and editor. He earned a Ph.D. in elementary particle physics at the University of Wisconsin in 1975, and a Master’s in journalism at Wisconsin in 1977. From 1977 to 1980 he was a writer and West Coast bureau chief for Chemical and Engineering News. From 1980 to 1991 he was a senior writer at Science magazine, where he covered physics, space, astronomy, computer science, artificial intelligence, molecular biology, psychology, and neuroscience. He was a freelance writer from 1991 to 2003 and from 2007 to 2008; in between he worked in media affairs for the National Science Foundation from 2003 to 2006. He was the editorial page editor at Nature magazine from 2008 to 2010, and a features editor at Nature until 2016. He is the author of Man-Made Minds (Walker, 1987), a book about artificial intelligence; Complexity (Simon & Schuster, 1992), a book about the Santa Fe Institute and the new sciences of complexity; and The Dream Machine (Viking, 2001), a book about the history of computing. He lives in Washington, D.C. with his wife, Amy E. Friedlander.
Interactive Discussion (please put you name before your comments):
Ahmet Erdemir -- My question is directed to Charles Taylor but is relevant to establishing translational roadmaps and for transferability of this dissemination experience to other models. When you raised venture capital, you likely had financial models to support the economic value of your model as well as its clinical value. If that economic value was not enticing to raise capital, what would be an alternative strategy for you to translate a useful but not necessarily cheaper innovation in modeling and simulation?
Ravi Radhakrishnan, UPenn, Comment on acceptance of multiscale modeling in decision making. Tina Morrison of FDA advocates for the term Digital Evidence, it is a term getting acceptance at the congressional levels.
Misha Pavel Northeastern University: As we are moving towards digital human it is important to start thinking about the connections of neuropsychological states to physiological and biomechanical processes. This is necessary to build models that would predict, assess and improve key health behaviors. To date, most of the psychological frameworks are data-driven focused on static processes, but we obviously need to have more efforts towards mechanistic, causal and dynamical models. Do you think the field, NIH and the proposal reviewers are ready to give these efforts a chance?
Some take-home points from the session:
- Tremendous potential for multi-scale modeling to help decision making. Each panelists offered their experiences of how this has occurred.
Here is a link to Dr. Slayton's CDC Vital Signs publication.
Here is a link to a Forbes article about nutrition policy work in Balitmore.
Here is a link to the Project Play 2020 Initative.
- Avoid using technical terms when describing models.
- Modeling can help bring together different decision makers.
- Show decision makers the data or the outcomes of the model to show them how the model can be useful.
- Secure champions who help translate the model and model results to others.
- Understand the needs and perspectives of decision makers.
Nice talk. Question to Dr.
Nice talk. Question to Dr. Taylor: any thoughts on how HeartFlow might be a means of addressing some of the controversy raised by the ORBITA trial (UK study questioning the benefit of PCI for stable angina)?
This is Mike Miga, from
This is Mike Miga, from Vanderbilt University, Department of Biomedical Engineering. Sorry I could not be there and I thank the organizers for allowing for the videocast and this forum. I enjoyed your talk Dr. Taylor, and I enjoyed panel opening comments. I liked the discussion of moving toward the clinic and making more model-based intervention. I have two discussion points for comment:
- Dr. Taylor has taken modeling expertise and essentially made a ‘service’, a very exciting translation. Nothing against the entrepreneur, but expert technologists are often staffed at major medical centers in a variety of roles – e.g. assisting in percutaneous valve choice in minimally invasive valve replacement, assisting in guidance technology within image-guided surgery, stereotactic EEG for epilepsy. Your technology is presented from a diagnostic standpoint, and it totally makes sense and is quite exciting, what is your vision for data and modeling on the more interventional and surgical side of treatment, wider areas of procedural medicine? What will be the nature of medical center teams be like in the future? Should we be expanding medical center expertise on treatment teams or should we be developing more of an outsourcing framework? I wonder if we should be looking at the nature of 'treatment team' expertise, is the better model to really move the expertise outside the medical center, and physicians become more integration based.
- Data is becoming constrained due to its value. Your technology as well as many other technologies getting noted in the popular media are testimonies to the power of that data. Are we going to see inhibition of innovation due to data availability (protectionist attitude of data)?
Thank you again for the session, wonderful!
Question to Charles, he
Question to Charles, he mentioned predictive methods in his talk to help with a specific diagnostic cardiovascular measure they found effective. He also showed economic impact on a population. Did his group develop risk equations that are more general and publicly available as code or reproducible algorithm? If yes, can he elaborate?
Very nice point by Dr.
Very nice point by Dr. Waldrop: MSMs/ABMs as objects to facilitate interaction and discourse. ABMs as Rhetorical Objects. Queston: What sort of methods available to establish trust among stakeholders for a MSM that is complex and relatively opaque? Can't just be confirmation against data, or reinforcing existing perspectives...
M. Mitchell mentioned that
M. Mitchell mentioned that the model building process is helpful for human understanding. Can he comment on human. Comprehension vs. machine comprehension. In other words, is the modeling effort an exercise to improve human capabilities or is the goal to transfer knowledge to machines?