2017 Seminars

March 17,2017- Rui Chen, Ph.D., Research Scientist, Samsung Research America.
From Academia to Industry: A Preview of Data Science
Abstract: Data science has been a buzzword in both academia and industry. In this talk, we will try to bridge the gap between academia and industry in the context of data science. We will explore the roles of data science in industry and show how to apply school knowledge to solve a real-world business problem on user growth. We demonstrate how to combine various computer science techniques to acquire, engage and retain users under a business funnel model.
Bio: Rui is a senior staff research scientist at Samsung Research America, where he leads a horizontal data science team that supports multiple Samsung products, including Samsung Pay and Samsung Health. He has published nearly 40 papers in top venues on databases, data mining and data security, such as PVLDB, VLDBJ, TKDE, ICDE, SIGKDD, CCS, ICDM, SDM, and CIKM. His papers have been cited more than 2,000 times. Prior to his post at Samsung, he was a research assistant professor in the Department of Computer Science at Hong Kong Baptist University and a postdoctoral fellow at the University of British Columbia. He was the recipient of CIKM 2015 Best Paper Runner Up and the Alexander Graham Bell Canada Graduate Scholarship issued by Natural Sciences and Engineering Research Council of Canada.

March 10, 2017-
David Classen, M.D., CMIO, Pascal Metrics Inc.
Harnessing Health IT to Improve Patient Safety
Abstract: This presentation will focus on the safety of Health IT Systems. A recent IOM report focused on Health IT and Patient Safety questioned whether the broad adoption of Health IT in the last decade had actually improved the safety of care and cited instances of where these systems had actually harmed patients. This presentation will review work on assessing safety improvements with Health IT and what the future for Health IT and safety may look like.
Bio: David C. Classen, M.D., M.S. Dr, Classen is the CMIO at Pascal Metrics, a Patient Safety Organization (PSO) and an Associate Professor of Medicine at the University of Utah and an Active Consultant in Infectious Diseases at The University of Utah School of Medicine in Salt Lake City, Utah.

He received his medical degree from the University Of Virginia School Of Medicine and a Masters of Science degree in medical informatics from the University Of Utah School Of Medicine. He served as Chief Medical Resident at the University of Connecticut. He is board certified in Internal Medicine and Infectious Diseases. He developed the medication safety programs at Intermountain Healthcare; He was the chair of Intermountain Health Cares Clinical Quality Committee for Drug Use and Evaluation and was also the initial developer of patient safety research and patient safety programs at Intermountain Healthcare. In addition he developed, implemented and evaluated a computerized physician order entry program at LDS Hospital that significantly improved the safety of medication use.

He was a member of the Institute of Medicine Committee (IOM) that developed the National Healthcare Quality Report and he was also a member of the Institute of Medicine Committee on Patient Safety Data Standards. He was recently a member of the Institute of Medicine Committee on Health Information Technology and Patient Safety.

He chaired the QUIC (Federal Safety Taskforce)/IHI Collaborative on Improving Safety in High Hazard Areas. Dr. Classen was Co Chair of the Institute of Healthcare Improvements Collaborative on Perioperative Safety and the Surgical Safety Collaborative at the Institute of Healthcare Improvement (IHI). He was also a faculty member of the IHI/National Health Foundation Safer Patients Initiative in the United Kingdom. In addition Dr. Classen is one of the developers of the “Trigger Tool Methodology” at IHI, used for the improved detection of adverse events which is currently being used by more than 500 different healthcare organizations through out the Unites States and Europe.

He currently co chairs the National Quality Forum’s AHRQ Common Formats Committee and Dr. Classen is an advisor to the Leapfrog Group and has developed and implemented the CPOE/EHR flight simulator for AHRQ and National Quality Forum. This Electronic Health Record (EHR) Flight simulator has been used to evaluate hundreds of inpatient and ambulatory EHR systems after implementation across the United States and The United Kingdom and is a critical part of the National Quality Forum’s Safe Practice #16 for Computerized Provider Order Entry within EHRs.

March 3,2017- David Page, Ph.D., Professor, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
High-Throughput Machine Learning from EHR Data 
Abstract: The widespread use of electronic health records and the many recent successes of machine learning raise at least two natural questions. How well can future health events of patients be predicted from EHR data, at various lengths of time in advance? And how can such predictions improve human health? This talk answers the first question via a new approach called "high-throughput machine learning," and it speculates about answers to the second question. In particular, this talk argues that many healthcare applications require not just accurate prediction, but accurate prediction by causally-faithful models. Causal discovery from observational data is already a major research direction in machine learning and statistics, and this talk discusses new approaches across the spectrum from when "we know all the relevant variables" to when "we know only one relevant variable" for the task at hand. If time permits, the talk will also touch on the issue of protecting patient privacy while empowering the construction of accurate predictive models.
David Page is a Vilas Distinguished Achievement Professor at the University of Wisconsin-Madison. His primary appointment is in the Dept. of Biostatistics and Medical Informatics in the School of Medicine and Public Health, with an appointment in the Dept. of Computer Sciences where he teaches machine learning. His PhD in CS is from the University of Illinois at Urbana- Champaign, and he became involved in biomedical applications of machine learning as a post-doc in what was then the Computing Laboratory at Oxford University. He directs the Cancer Informatics Shared Resource of the Carbone Cancer Center and is a member of the Genome Center of Wisconsin. He previously served on the NIH's BioData Management and Analysis Study Section and the scientific advisory boards for the Wisconsin Genomics Initiative and the Observational Medical Outcomes Partnership, as well as the editorial boards for Machine Learning and Data Mining and Knowledge Discovery. He currently is on the National Library of Medicine Study Section (BLIRC) and directs the EHR project within UW-Madison's BD2K Center for Predictive Computational Phenotyping.

February 24,2017- Mary Devereaux, Ph.D., Assistant Director, Research Ethics Program, UCSD
Patient Consent in the Era of Big Data
Abstract: With the move to electronic health records (EHRs) and the growing capacity to gather and process terabytes of medical information, researchers understandably wish to access aggregated data to analyze patient outcomes, health demographics, and the economics of health care (Safran et al. 2007). But all medical information, whether sensitive or not, is governed by legal requirements for privacy, confidentiality, and security, e.g., HIPAA. The use of patient data for research also raises a host of ethical issues. Some patient information may be highly confidential, including family history, genetic testing results, a diagnosis of addiction, or immigration status. This talk examines ethical issues in the secondary use of personal health information gathered for clinical purposes. In particular, discussion will focus on what patients understand — and expect — regarding the de-identification and security of their information.
Bio: Mary Devereaux, Ph.D., is a philosopher and bioethicist at University of California, San Diego (UCSD). She is Assistant Director of the UCSD Research Ethics Program and the San Diego Research Ethics Consortium, and Director of the UCSD Biomedical Ethics Seminars. She also holds an appointment in the Health Law & Policy Program, where she is Academic Coordinator, and Adjunct Professor of Law at California Western School of Law. Devereaux serves on the Hospital Ethics Committee at the Medical Center Hillcrest and provides ethics training in the School of Medicine and a variety of graduate programs  in health sciences. She is founder and Director of Tough Cases, a monthly case-based ethics discussion for clinicians and medical staff, and Co-Director of the Medical Humanities Research Group on campus. She speaks widely on issues in biomedical and research ethics for academic and lay audiences. Recent publications include work on ethical and regulatory issues in stem cell research, reproductive medicine, cosmetic surgery, and medical tourism. Devereaux is a member of the American Society for Bioethics and Humanities and the American Philosophical Association.

February 17,2017-
Michael Hogarth, M.D., Professor, Department of Pathology and Laboratory Medicine, UC Davis Medical Center EDRS
The OneSource Initiative: An Approach to Structured Sourcing of Key Data in Electronic Health Records 
Abstract: Some of the most widely used electronic health record (EHR) systems in the US have significant shortcomings as information systems. Current systems have three key deficiencies regarding sourcing and managing of key data. First, key data elements are difficult to find in the record, or not in the record at all. Second, when key data is in the record, it may have multiple different states/values in different places, making it difficult to determine which one is “true". Third, the majority of key data elements for the appropriate management of major conditions are in unstructured text, making it is difficult to use these data for computer-assisted interventions. In addition to these shortcomings for key data, "core data element collections" for particular conditions have not been standardized in order to ensure they exist in the record. The OneSource Initiative is a collaborative project involving UCSF, FDA, CDISC and ONC to address these shortcomings and render core key data elements in computable form to support a broad range of processes such as coordination of care, population health analytics, and clinical research.
Bio: Dr. Hogarth is a board certified in Internist in the UC Davis Division of General Medicine and health informatics faculty in the Department of Pathology and Laboratory Medicine. He has taught health informatics at UC Davis for over 20 years. He also currently serves the UC Davis Health System as informatics and data quality lead for the Healthcare Analytics group. Dr. Hogarth has been involved in several large-scale informatics initiatives including California Electronic Death Registration System, the Athena Breast Health Network project, the I-SPY2 adaptive breast cancer clinical trial, UC-ReX, pSCANNER, and the California Precision Medicine Consortium. In collaboration with UC Santa Barbara, his team is currently building a new Birth Registration system for California.

February 10, 2017- Kai Zheng, Ph.D., Associate Professor, Department of Informatics, UCI

Electronic Health Records Adoption and the Implications for Health Services Research
Abstract: Most U.S. hospitals and clinics have by now implemented electronic health records (EHR) as a result of the recent policy mandate. While adoption of EHR introduces numerous benefits (e.g., elimination of illegible handwritten notes), it is also associated with a number of unintended adverse consequences, including a detrimental effect on the quality of clinical data recorded. Some notable causes for this effect are coexistence of paper forms and EHR; clinicians’ lack of knowledge of, or enthusiasm in, entering data in a rigid, structured format; and inaccurate coding due to a prominent emphasis on revenue management in many EHR systems currently in use. In this talk, I will showcase some manifestations of the diminished quality of clinical data in the post-EHR era, and their potential impact on clinical, translational, and health services research.
Bio: Kai Zheng PhD is Associate Professor of Informatics in the Department of Informatics at the University of California, Irvine (UCI). He co-directs the Center for Biomedical Informatics at the UCI Institute for Clinical and Translational Science. Zheng’s research draws upon techniques from the fields of information systems and human–computer interaction to study the use of information, communication, and decision technologies in patient care delivery and management. His recent work has focused on topics such as interaction design, workflow and sociotechnical integration, and diffusion and evaluation of health IT. Zheng received his PhD degree in Information Systems from Carnegie Mellon University.

February 3, 2017- Jejo Koola, M.D., Assistant professor, DBMI, UCSD
Principles and Practices of Data Visualization in Medical Informatics
Abstract: The complexity of data has grown exponentially over the last 30 years. These data allow us to predict disease, identify treatments, and make prognoses. However, with the ever larger datasets also come ever more complicated models that stretch the abilities of human cognition. A central task in information visualization is to find the appropriate visualization paradigm for both the data and the problem scenario at hand. We will explore several central paradigms for the visualization and analysis of healthcare data. In addition, we will demonstrate practical toolsets to employ for these visualization tasks.
Bio: Jejo Koola is a practicing internist in the field of hospital medicine and clinical informatics. He received his medical degree from the Medical University of South Carolina and completed his residency at the Medical College of Virginia. He completed a fellowship in Biomedical Informatics through the Department of Veterans Affairs in conjunction with Vanderbilt University. His is focused on using informatics tools (including predictive analytics, natural language processing, and information visualization) to improve the care of multi-morbid hospital patients. He has published in several clinical and informatics journals.

January 27,2017- Jing Zhang, NLM Ph.D. fellow, DBMI, UCSD
Supporting Information Needs of Transitional Phases in Diabetes Management in Online Health Communities
Abstract: As of 2014, 29.1 million people in the US have diabetes. Diabetes has a substantial and increasing impact on the quality of life. Patients face the burden of self-management and the challenge of ‘transitional’ phases, when they need to find out about their options and the next course of action. The field has under-explored the specific information needs patients have during those transitional phases. I aim to investigate the information needs in the transitional phases and develop design requirements in providing balanced and comprehensive information to better support patient information needs.
Bio: I am a PhD candidate at DBMI. My research interests lie in the intersection of health informatics and human-computer interaction. I am specifically interested in studying how clinicians, patients and health consumers use technology, with the goal of improving system design and human performance. I am currently working with Dr. Jina Huh on online health communities and will also start on a usability project.

Zhanglong Ji, Ph.D. fellow, DBMI, UCSD
Beacon Service Against Bustamante Attack
Abstract: Genome data sharing has been strictly confined due to privacy concerns. Recent researches have proven that even releasing existence of alleles in a database may leak the existence of a person. In this presentation, I will present three algorithms to protect data privacy given different assumptions. In all three algorithms, the attackers have genome information of a patient whom he wants to know whether is in the database. The first algorithm assumes attackers do not have any information on allele frequency; the second one assumes attackers know those frequencies, and have correct genome information. The last one assumes attackers have both allele frequencies and genome frequencies with some random errors.
Bio: Zhanglong Ji is a PhD student in Computer Science program right now. His research interest is in machine learning and privacy protection. Before coming to UC San Diego, he majored in Statistics in Peking University.

January 20, 2017-
Mike Zaroukian, M.D., Ph.D., Vice President & CMIO Sparrow Health System
Electronic Health Record Usability and Healthcare's "Quadruple" Aim
Abstract: Achieving the “Triple Aim” of improved patient experience, improved population health, and reducing per-capita health care spending in an era of healthcare delivery and payment transformation requires a synergy between people, processes and technologies to ensure that every patient receives high quality and high value care, every time. Over the past 6 years, the incentives for rapid adoption and expectations for robust use of electronic health record (EHR) systems and other health IT has been accompanied by increasing reports of physician burnout and complaints that poor EHR usability is contributing to the problem. This has prompted a call for a fourth aim related to improving clinician satisfaction. To facilitate achievement of this so-called “Quadruple Aim”, health IT professionals should be grounded in the concepts of EHR usability, user experience, and user-centered design from the clinician’s perspective. In this presentation, we will discuss health IT usability, why it matters, developing a framework to approach EHR usability challenges and opportunities, and how clinicians and IT professional can work together to improve usability in support of the “Quadruple Aim”.
Bio:  Dr. Zaroukian is VP & CMIO at Sparrow and a Professor of Medicine at Michigan State University. A practicing primary care physician board-certified in internal medicine and clinical informatics, he provide physician executive leadership to EMR implementation and optimization, along with other health IT capabilities to transformation care. Dr. Zaroukian has published research and given hundreds of national and international presentations on the use of health IT to improve patient care, research, education and administration. He is a former residency director and previous recipient of the HIMSS Physician IT Leadership Award, a "Top 25 Clinical Informaticists" award, and an AMIA Leadership Award. He is current Chair of the HIMSS North America Board of Directors and Chair-elect of the global HIMSS Board of Directors. He is a former member of the American Medical Association (AMA) Health Information Technology Advisory Group, past chair of the American College of Physicians Medical Informatics Committee, and served on the ONC Health IT Policy Committee Advanced Health Models and Meaningful Use Workgroup. He currently serves on the “Optimizing Strategies for Clinical Decision Support” workgroup for the National Academy of Medicine Leadership Consortium for a Value & Science-Driven Health System.

January 13, 2017-
Nansu Zong, Ph.D., Postdoctoral Fellow, DBMI, UCSD

Predict Novel Drug-Target Associations Based on Heterogeneous Networks of Biomedical Linked Data 
Abstract: A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. We introduce a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within a tripartite heterogeneous network generated from the biomedical linked data. With the execution of the four-step experimental design, this proposed method shows promising results for drug-target association prediction.
Bio: Dr. Zong is a postdoctoral researcher in department of biomedical informatics, school of medicine, university of California, San Diego. He obtained the Ph.D. in computer science and engineering from Seoul National University. His research focuses on Big Data, Semantic Web and Linked Data, Data processing, management, and mining in Biomedical domain.