Award for outstanding research as a fellow and the Western Society for Clinical Investigation Outstanding Investigator award, and is a member of the American Society for Clinical Investigation.

Andrew Beam, PhD, is an assistant professor in the Department of Epidemiology at the Harvard T. H. Chan School of Public Health, with secondary appointments in the Department of Biomedical Informatics at the Harvard Medical School and the Department of Newborn Medicine at Brigham and Women’s Hospital. His research develops and applies machine learning methods to extract meaningful insights from clinical and biological datasets, and he is the recipient of a Pioneer Award from the Robert Wood Johnson Foundation for his work on medical artificial intelligence. Previously he was a senior fellow at Flagship Pioneering and the founding head of machine learning at VL56, a Flagship-backed venture that seeks to use machine learning to improve our ability to engineer proteins. He earned his PhD in 2014 from North Carolina State University for work on Bayesian neural networks, and he holds degrees in computer science (BS), computer engineering (BS), electrical engineering (BS), and statistics (MS), also from North Carolina State University. He completed a postdoctoral fellowship in biomedical informatics at the Harvard Medical School and then served as a junior faculty member. Dr. Beam’s group is principally concerned with improving, streamlining, and automating decision making in health care through the use of quantitative, data-driven methods. He does this through rigorous methodological research coupled with deep partnerships with physicians and other members of the health care workforce. As part of this vision, he works to see these ideas translated into decision-making tools that doctors can use to better care for their patients.

Paul Bleicher, MD, PhD, is a strategic advisor to OptumLabs. Dr. Bleicher was formerly the chief executive officer of OptumLabs since its inception. Prior to OptumLabs, he was the chief medical officer for Humedica, a next-generation clinical informatics company. He also co-founded and was a leader at Phase Forward, which was instrumental in transforming pharmaceutical clinical trials from paper to the web. Dr. Bleicher has served as a leader in industry organizations such as the National Academy of Medicine’s Leadership Consortium for Value & Science-Driven Health Care and the Drug Information Association. He has received numerous awards for his industry leadership. Dr. Bleicher holds a BS from Rensselaer, as well as an MD and a PhD from the University of Rochester School of Medicine and Dentistry. He began his career as a physician/investigator and an assistant professor at the Massachusetts General Hospital and the Harvard Medical School.

Medical Informatics Association, the Yearbook of Medical Informatics, and the American College of Physicians, among others. In the face of ever escalating complexity in medicine, informatics solutions are the only credible approach to systematically address challenges in health care. Tapping into real-world clinical data like electronic medical records with machine learning and data analytics will reveal the community’s latent knowledge in a reproducible form. Delivering this back to clinicians, patients, and health care systems as clinical decision support will uniquely close the loop on a continuously learning health system. Dr. Chen’s group seeks to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches that will deliver better care than either can do alone.

Guilherme Del Fiol, MD, PhD, is currently an associate professor and the vice-chair of research in the University of Utah’s Department of Biomedical Informatics. Prior to the University of Utah, Dr. Del Fiol held positions in clinical knowledge management at Intermountain Healthcare and as faculty at the Duke Community and Family Medicine Department. Since 2008, he has served as an elected co-chair of the Clinical Decision Support Work Group at Health Level International (HL7). He is also an elected fellow of the American College of Medical Informatics and a member of the Comprehensive Cancer Center at Huntsman Cancer Institute. Dr. Del Fiol’s research interests are in the design, development, evaluation, and dissemination of standards-based clinical decision support interventions. He has been focusing particularly in clinical decision support for cancer prevention. He is the lead author of the HL7 Infobutton Standard and the project lead for OpenInfobutton, an open-source suite of infobutton tools and web services, which is in production use at several health care organizations throughout the United States, including Intermountain Healthcare, Duke University, and the Veterans Health Administration. His research has been funded by various sources including the National Library of Medicine, the National Cancer Institute, the Agency for Healthcare Research and Quality, the Centers for Medicare & Medicaid Services, and the Patient-Centered Outcomes Research Institute. He earned his MD from the University of Sao Paulo, Brazil; his MS in computer science from the Catholic University of Parana, Brazil; and his PhD in biomedical informatics from the University of Utah.

Hossein Estiri, PhD, is a research fellow with the Laboratory of Computer Science (LCS) and an informatics training fellow of the National Library of Medicine. Dr. Estiri’s research involves designing data-driven systems for clinical decision making and health care policy. His recent work has focused on designing and developing visual analytics programs (VET, DQe-c, DQe-v, and DQe-p)

to explore data quality in electronic health record (EHR) data. His research with LCS is focused on applying statistical learning techniques (Deep Learning and unsupervised clustering) and data science methodologies to design systems that characterize patients and evaluate EHR data quality. Dr. Estiri holds a PhD in urban planning and a PhD track in statistics from University of Washington. Prior to joining LCS, Dr. Estiri completed a 2-year postdoctoral fellowship with the University of Washington’s Institute of Translational Health Sciences and Department of Biomedical Informatics.

James Fackler, MD, is an associate professor of anesthesiology and critical care medicine and pediatrics at the Johns Hopkins University School of Medicine. His areas of clinical expertise include acute respiratory distress syndrome, novel respiratory therapies, and signal fusion and monitoring. Dr. Fackler received his undergraduate degree in biology from the University of Illinois and earned his MD from Rush Medical College in Chicago. He completed his residency in anesthesiology and performed a fellowship in pediatric intensive care and pediatric anesthesia at the Johns Hopkins University School of Medicine. Dr. Fackler joined the Johns Hopkins faculty in 2006. He worked for the Cerner Corporation from 2002 to 2006 and left the position of vice president to return to academic medicine. He founded Oak Clinical Informatics Systems and consults for other device and information integration companies. Dr. Fackler’s research interests include optimizing patient surgical services by analyzing mathematical models of patient flow through hospitals, on either a scheduled or an emergency basis. He serves as the editor for Pediatric Critical Care Medicine and as an ad hoc journal reviewer for many notable publications including New England Journal of Medicine and Critical Care Medicine. He is a member of the American Association of Artificial Intelligence, the American Medical Informatics Association, and the Society for Critical Care Medicine. Dr. Fackler is a frequent lecturer and panelist on the subject of critical care informatics. He is an expert in data integration.

Stephan Fihn, MD, MPH, attended St. Louis University School of Medicine and completed an internship, residency, and chief residency at the University of Washington (UW). He was a Robert Wood Johnson Foundation Clinical Scholar and earned a master’s degree in public health at UW where he is professor of medicine and health services and the head of the Division of General Internal Medicine. During a 36-year career with the U.S. Department of Veterans Affairs (VA), Dr. Fihn held a number of clinical, research, and administrative positions. He directed one of the first primary care clinics in the VA and for 18 years led the Northwest VA Health Services Research & Development Center of Excellence

at the Seattle VA. He also served several national roles in the Veterans Health Administration including acting chief research and development officer, chief quality and performance officer, director of analytics and business, and director of clinical system development and evaluation. His own research has addressed strategies for improving the efficiency and quality of primary and specialty medical care and understanding the epidemiology of common medical problems. He received the VA Undersecretary’s Award for Outstanding Contributions in Health Services Research in 2002. He has published more than 300 scientific articles and book chapters and two editions of a textbook titled Outpatient Medicine. He is deputy editor of JAMA Network Open. He is active in several academic organizations including the Society of General Internal Medicine (SGIM) (past-president), the American College of Physicians (fellow), the American Heart Association (fellow), and AcademyHealth. He received the Elnora M. Rhodes Service Award and the Robert J. Glaser Award from SGIM.

Anna Goldenberg, MA, PhD, is a Russian-born computer scientist and an associate professor at the University of Toronto’s Department of Computer Science and the Department of Statistics, a senior scientist at the Hospital for Sick Children’s Research Institute, and the associate research director for health at the Vector Institute for Artificial Intelligence. She is the first chair in biomedical informatics and artificial intelligence at the Hospital for Sick Children. Dr. Goldenberg completed a master’s in knowledge discovery and data mining, followed by a PhD in machine learning at Carnegie Mellon University in Pittsburgh, where her thesis explored scalable graphical models for social networks. Dr. Goldenberg moved to Canada in 2008 as a postdoctoral fellow. She is currently appointed as an associate professor at the University of Toronto’s Department of Computer Science and the Department of Statistics and a scientist at the Hospital for Sick Children’s Research Institute. Her laboratory explores how machine learning can be used to map the heterogeneity seen in various human diseases, specifically to develop methodologies to identify patterns in collected data and improve patient outcomes. She has more than 50 publications in peer-reviewed journals. Similarity Network Fusion, a networking method devised by her research group is the first data integration method developed to integrate patient data that improved survival outcome predictions in different cancers. She has an h-index of 17, and her research has been cited more than 2,000 times. In 2017, Dr. Goldenberg was appointed as a new Tier 2 CIHR-funded Canada Research Chair in Computational Medicine at the University of Toronto. On January 15, 2019, Dr. Goldenberg was named the first chair in biomedical informatics and artificial intelligence at the Hospital for Sick Children, which is the first of its kind to exist in a Canadian children’s hospital.

This position is partially funded by a $1.75 million donation from Amar Varma (a Toronto entrepreneur whose newborn son underwent surgery at the Hospital for Sick Children).

Seth Hain, MS, leads Epic’s analytics and machine learning research and development. This includes business intelligence tools, data warehousing software, and a foundational platform for deploying machine learning across Epic applications. Alongside a team of data scientists and engineers, he focuses on a variety of use cases ranging from acute care and population health to operations and improving workflow efficiency.

Jaimee Heffner, PhD, is a clinical psychologist who researches tobacco-cessation interventions for populations who experience health disparities, including people with mental health conditions, low-income veterans, and sexual and gender minorities. Much of her work focuses on new behavioral treatments such as acceptance and commitment therapy and behavioral activation. She develops methods to deliver these interventions—such as websites, smartphone apps, and other forms of technology—to improve the accessibility of treatment for all tobacco users. Her research interests also include implementation of tobacco-cessation interventions in the novel setting of lung cancer screening.

Sonoo Thadaney Israni, MBA, is an intrapreneur at Stanford University. She works with faculty leadership to thought partner and launches new centers, initiatives, academic programs, and more. Currently, she serves as the executive director for Dr. Abraham Verghese’s portfolio, including a new center—Presence: The Art and Science of Human Connection in Medicine. She focuses on the intersection of technology, equity, and inclusion. Her intrapreneurial successes at Stanford include the MSc in Community Health and Prevention Research; the Stanford Women and Sex Differences in Medicine Center; the Diversity and First-Gen Office (serving Stanford students who are first in their family to attend college); the Restorative Justice Pilot; and more. She teaches coursework in leveraging conflict for constructive change, leadership skills, and mediation. Ms. Israni co-chairs the National Academy of Medicine’s Artificial Intelligence in Healthcare Working Group and co-shepherds its Technology Across the Lifecourse Group. She also serves on the Association of American Medical Colleges Restorative Justice for Academic Medicine Committee teaching curricula to address diversity in health care. She spent more than 25 years in Silicon Valley before coming to Stanford University in 2008. Ms. Israni’s academic work includes an MBA, a BA in psychology with minors in sociology and education, and a postbaccalaureate in mass communications. She is also a trained mediator and restorative justice

practitioner for the State of California, serving as the co-chair of the Commission on Juvenile Delinquency and Prevention for San Mateo County.

Edmund Jackson, PhD, is the HCA Healthcare chief data scientist and the vice president of data and analytics within the Clinical Services Group. His education is a BscEng and MScEng, both in electronic engineering, followed by a PhD in statistical signal processing from Cambridge University. In that work, Dr. Jackson focused on applications of sequential Markov chain methods in bioinformatics. He pursued a career as a quantitative analyst in the hedge fund industry for several years. More recently, Dr. Jackson has sought more meaningful work and found it at HCA, where his remit is to create algorithms and systems to improve the quality of clinical care, operational efficiency, and financial performance of the firm through better utilization of data.

Jeffrey Klann, PhD, focuses his work with the Laboratory of Computer Science on knowledge discovery for clinical decision support, sharing medical data to improve population health, revolutionizing user interfaces, and making personal health records viable. Dr. Klann holds two degrees in computer science from the Massachusetts Institute of Technology and a PhD from Indiana University in health informatics. He completed a National Library of Medicine Research Training Fellowship concurrently with his PhD. He holds faculty appointments at the Harvard Medical School and Massachusetts General Hospital.

Rita Kukafka, DrPH, MA, FACMI, is a professor of biomedical informatics and sociomedical sciences at the Mailman School of Public Health at Columbia University. Dr. Kukafka received her bachelor’s degree in health sciences from Brooklyn College, a master’s degree in health education from New York University, and a doctorate in public health with a concentration in sociomedical sciences from the Mailman School of Public Health at Columbia University. Nearly a decade after receiving her doctorate, she returned to Columbia where she completed a National Library of Medicine postdoctoral fellowship, and received a master’s degree in biomedical informatics. Having worked at public health agencies and academia, and leading large-scale population health interventions, she was convinced then and remains convinced that public health’s “winnable battles” are amenable to informatics solutions. For the duration of her training to the present, Dr. Kukafka has been involved in leadership roles at the national level to influence the growth and direction of public health informatics. At Columbia, Dr. Kukafka holds joint appointments with the Department of Biomedical Informatics and the Mailman School of Public Health (sociomedical sciences). She served as the director of the graduate training program from 2008 to 2013.

She is also the director of the Health Communication and Informatics Laboratory at the Department of Biomedical Informatics and certificate lead for Public Health Informatics at Mailman. Her research interests focus on patient and community engagement technologies, risk communication, decision science, and implementation of health promoting and disease prevention technologies into clinical workflow. Her projects include developing decision aids, portals for community engagement, requirement and usability evaluation, and mixed-method approaches to studying implementation and outcomes. Dr. Kukafka is an elected member of the American College of Medical Informatics and the New York Academy of Medicine. She has been an active contributor to the American Medical Informatics Association (AMIA), and is an AMIA board member. She has chaired the Consumer Health Informatics Working group for AMIA, and served on an Institute of Medicine committee that authored the report Who Will Keep the Public Healthy?: Educating Public Health Professionals for the 21st Century. Dr. Kukafka has authored more than 100 articles, chapters, and books in the field of biomedical informatics including a textbook (Consumer Health Informatics: Informing Consumers and Improving Health Care, 2005, with D. Lewis, G. Eysenbach, P. Z. Stavri, H. Jimison, and W. V. Slack. New York: Springer).

Hongfang Liu, PhD, is a professor of biomedical informatics in the Mayo Clinic College of Medicine, and is a consultant in the Department of Health Sciences Research at the Mayo Clinic. As a researcher, she is leading the Mayo Clinic’s clinical natural language processing (NLP) program with the mission of providing support to access clinical information stored in unstructured text for research and practice. Administratively, Dr. Liu serves as the section head for Medical Informatics in the Division of Biomedical Statistics and Informatics. Dr. Liu’s primary research interest is in biomedical NLP and data normalization. She has been developing a suite of open-source NLP systems for accessing clinical information, such as medications or findings from clinical notes. Additionally, she has been conducting collaborative research in the past decade in utilizing existing knowledge bases for high-throughput -omics profiling data analysis and functional interpretation. Dr. Liu’s work in informatics has resulted in informatics systems that unlock clinical information stored in clinical narratives. Her work accelerates the pace of knowledge discovery, implementation, and delivery for improved health care.

Michael Matheny, MD, MS, MPH, is a practicing general internist and a medical informatician at Vanderbilt University and the Tennessee Valley Healthcare System of the U.S. Department of Veterans Affairs (VA). He received a BS in chemical engineering and an MD from the University of Kentucky, completed

internal medicine residency training at St. Vincent’s, Indianapolis, Indiana, and was a National Library of Medicine Biomedical Informatics Fellow at the Decision Systems Group at Brigham & Women’s Hospital, Boston, Massachusetts, during which time he completed a master’s degree in public health at Harvard University as well as a master’s degree of science in biomedical informatics at the Massachusetts Institute of Technology. He has expertise in developing and adapting methods for postmarketing medical device surveillance, and has been involved in the development, evaluation, and validation of automated outcome surveillance statistical methods and computer applications. He is leading the OMOP extract, transform, and load team within VINCI for the national Veterans Health Administration data, and is a co-principal investigator for the pScanner CDRN Phase 2. He also is currently independently funded for two VA HSR&D IIR’s in automated surveillance and data visualization techniques for acute kidney injury following cardiac catheterization and patients with cirrhosis. His key focus areas include natural language processing, data mining, and population health analytics as well as health services research in acute kidney injury, diabetes, and device safety in interventional cardiology.

Douglas McNair, MD, PhD, serves as a senior advisor in quantitative sciences—Analytics Innovation in Global Health at the Bill & Melinda Gates Foundation. He assists the foundation’s research and development in drug and vaccine development for infectious diseases, childhood diseases, and neglected tropical diseases. Current projects include product development programs in discovery and translational sciences involving Bayesian networks. His activity also includes machine learning and modeling of health economics, collaborating with the Global Development division. Previously, Dr. McNair was the president of Cerner Math Inc., responsible for the artificial intelligence components of Cerner’s electronic health record (EHR) solutions, discovering artificial intelligence predictive models from real-world de-identified EHR-derived big data. Dr. McNair is the lead inventor on more than 100 patents and pending patent applications, including several involving Bayesian predictive models for clinical diagnostics.

Eneida Mendonça, MD, PhD, received her MD from the Federal University of Pelotas in Brazil and her PhD in biomedical informatics in 2002 from Columbia University in New York. Dr. Mendonça pioneered the use of natural language processing in both biomedical literature and in electronic medical record narratives in order to identify knowledge relevant to medical decision making in the context of patient care. In addition, she has devoted many years to developing innovative clinical information systems that have been

integrated in New York-Presbyterian Hospital, Columbia University Medical Center, and Cornell Medical Center. Most recently, Dr. Mendonça was an associate professor of pediatrics at the University of Chicago. Dr. Mendonça will begin to develop a program in medical/clinical informatics in both research and training under the National Institutes of Health–funded Institute for Clinical and Translational Research (ICTR) in which the Department is a core unit and the College of Engineering is an ICTR partner.

Joni Pierce, MBA, is a principal at J. Pierce and Associates and adjunct faculty at the University of Utah’s David Eccles School of Business. Ms. Pierce received her MBA from the University of Utah and is currently pursuing a master’s degree in biomedical informatics and clinical decision support.

W. Nicholson Price II, JD, PhD, is an assistant professor of law at the University of Michigan Law School, where he teaches patents and health law and studies life science innovation, including big data and artificial intelligence in medicine. Dr. Price is co-founder of regulation and innovation in the biosciences; co-chair of the Junior IP Scholars Association; co-lead of the Project on Precision Medicine, Artificial Intelligence, and Law at the Harvard Law School’s Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and a core partner at the University of Copenhagen’s Center for Advanced Studies in Biomedical Innovation Law.

Joachim Roski, PhD, MPH, delivers solutions in the areas of care transformation, health care business, and outcome analytics; quality/safety; and population health improvement. He supports a range of clients in their health care planning, improvement, strategic measurement, analysis, and evaluation needs. His clients include the Military Health Service, the Veterans Health Administration, the Centers for Medicare & Medicaid Services, The Office of the National Coordinator of Health Information Technology, and others. He is a well-published national expert who speaks frequently on the topics of measuring and improving health care costs, quality/safety, outcomes, and value.

Suchi Saria, PhD, MSc, is a professor of machine learning and health care at Johns Hopkins University, where she uses big data to improve patient outcomes. Her interests span machine learning, computational statistics, and its applications to domains where one has to draw inferences from observing a complex, real-world system evolve over time. The emphasis of her research is on Bayesian and probabilistic graphical modeling approaches for addressing challenges associated with modeling and prediction in real-world temporal systems. In the past 7 years,

she has been particularly drawn to computational solutions for problems in health informatics as she sees a tremendous opportunity there for high impact work. Prior to joining Johns Hopkins, she earned her PhD and master’s degree at Stanford University in computer science, working with Dr. Daphne Koller. She also spent 1 year at Harvard University collaborating with Dr. Ken Mandl and Dr. Zak Kohane as a National Science Foundation Computing Innovation Fellow. While in the Valley, she also spent time as an early employee at Aster Data Systems, a big data startup acquired by Teradata. She enjoys consulting and advising data-related startups. She is an investor and an informal advisor to Patient Ping.

Nigam Shah, MBBS, PhD, is an associate professor of medicine (biomedical informatics) at Stanford University, an assistant director of the Center for Biomedical Informatics Research, and a core member of the Biomedical Informatics Graduate Program. Dr. Shah’s research focuses on combining machine learning and prior knowledge in medical ontologies to enable use cases of the learning health system. Dr. Shah received the American Medical Informatics Association New Investigator Award for 2013 and the Stanford Biosciences Faculty Teaching Award for outstanding teaching in his graduate class on data-driven medicine. Dr. Shah was elected into the American College of Medical Informatics in 2015 and was inducted into the American Society for Clinical Investigation in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University, and completed postdoctoral training at Stanford University.

Ranak Trivedi, MA, MS, PhD, is a clinical health psychologist and a health services researcher interested in understanding how families and patients can better work together to improve health outcomes for both. Dr. Trivedi is also interested in identifying barriers and facilitators of chronic illness self-management, and developing family centered self-management programs that address the needs of both patients and their family members. Dr. Trivedi is also interested in improving the assessment and treatment of mental illnesses in primary care settings and evaluating programs that aim to improve these important activities.

Danielle Whicher, PhD, MHS, is a health researcher at Mathematica Policy Research. In this role, she participates in large-scale evaluations of national and state health payment and delivery reform initiatives. She is also engaged in efforts to evaluate health information technologies. Prior to joining Mathematica, Dr. Whicher was a senior program officer for the National Academy of Medicine (NAM) Leadership Consortium for a Value & Science Driven Health System, where she directed policy projects on a variety of topics related to the use of science and technology to inform health and health care. Before her work at the

NAM, Dr. Whicher held positions at the Patient-Centered Outcomes Research Institute, the Johns Hopkins Berman Institute for Bioethics, and the Center for Medical Technology Policy. She has a PhD and an MHS in health policy and management from the Johns Hopkins Bloomberg School of Public Health and currently serves as a co-editor for the journal Value in Health. Her work has been published in a variety of reports and peer-reviewed journals, including Annals of Internal Medicine, Medical Care, PharmacoEconomics, Clinical Trials, and The Journal of Law, Medicine, & Ethics.

Jenna Wiens, PhD, is a Morris Wellman Assistant Professor of Computer Science and Engineering at the University of Michigan in Ann Arbor. She is currently the head of the Machine Learning for Data-Driven Decisions research group. Dr. Wiens’s primary research interests lie at the intersection of machine learning and health care. She is particularly interested in time-series analysis, transfer/multitask learning, and causal inference. The overarching goal of her research agenda is to develop the computational methods needed to help organize, process, and transform data into actionable knowledge. Dr. Wiens received her PhD in 2014 from the Massachusetts Institute of Technology (MIT). At MIT, she worked with Professor John Guttag in the Computer Science and Artificial Intelligence Lab. Her PhD research focused on developing accurate patient risk-stratification approaches that leverage spatiotemporal patient data, with the ultimate goal of discovering information that can be used to reduce the incidence of health care–associated infections. In 2015, Dr. Wiens was named one of Forbes’ 30 under 30 in Science and Healthcare; she received a National Science Foundation CAREER Award in 2016; and this past year was named to the MIT Tech Review’s list of 35 Innovators Under 35.

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