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Guest Lecture

Presenter:

Dr. Jessica Tenenbaum

Date:

02-21-2019

Time:

3:30PM-5:00PM

Location:

Jesse Wrench Auditorium, Memorial Union

Health 3.0: Enabling precision medicine through translational bioinformatics and the learning health system

A confluence of technological, computational and legislative advances have put us on the horizon of an exciting time in biomedical research and healthcare, with increasingly blurred boundaries between the two. Advances in experimental technologies enable observation across tens of thousands of molecules at a time. Pervasive mobile devices and an ever-expanding landscape of activity and health-related apps are generating terabytes of data outside of traditional clinical care providers. Advances in computational power and parallel computing facilitate the analysis and interpretation of these diverse streams of data. And an evolving legislative landscape has led to the rapid uptake of electronic health record (EHR) technology nation-wide, as well as complex and ever-changing laws regarding how EHR data may be used and exchanged for clinical care, quality improvement, and secondary research. This talk will describe key advances in these areas, examples of how some of these advances have affected clinical guidelines and actual patients. It will also describe early work in mining EHR data to enable a precision medicine approach to mental illness. The speaker will conclude by discussing how these advances are helping to realize the vision of precision medicine- redefining disease to enable the right intervention for the right person at the right time.

Bio Dr. Tenenbaum is a faculty member in the Division of Translational Biomedical Informatics in the Department of Biostatistics and Bioinformatics at Duke University . Her primary research interests are  1. Informatics to enable precision medicine; 2. Mental health informatics; 3. Infrastructure and standards to enable research collaboration and integrative data analysis; and 4. Ethical, legal, and social issues that arise in translational research, direct to consumer genetic testing, and data sharing. Current research projects focus on analyzing electronic health record data to better understand mental illness, target resource allocation, guide treatment, and develop targeted therapeutic interventions.

Nationally, Dr. Tenenbaum plays a leadership role in the American Medical Informatics Association, serving as Chair of the Mental Health Informatics Working Group and as an elected member of the Board of Directors. She is an Associate Editor for the Journal of Biomedical Informatics and served on the advisory panel for Nature Publishing Group’s Scientific Data initiative.  After earning her bachelor’s degree in biology from Harvard, Dr. Tenenbaum worked as a program manager at Microsoft Corporation in Redmond, WA for six years before pursuing a PhD in biomedical informatics at Stanford University.

Seminar Series

Presenter:

Marius Petruc

Date:

01-24-2019

Time:

3:30PM-4:30PM

Location:

Leadership Auditorium, 2501 Student Center

A Data Analytics Framework for Improving the Efficiency of Stroke Imaging Investigations

Emergency departments are under tremendous pressure to provide high-quality care in the shortest amount of time possible. While not all cases seen in the ED are urgent in nature, some require immediate attention. These true emergencies are usually complex in nature and depend on people, processes and technologies to work seamlessly, in perfect orchestration, in order to achieve the desired outcomes for the patient. One such condition is the stroke, a condition which left untreated (or treated incorrectly) can lead to devastating debilities and even death. To treat stroke successfully, a correct imaging diagnostic needs to be placed and treatment initiated within 3 hours of symptoms onset. Few other conditions in medicine have more stringent time requirements and fewer depend more heavily on timely imaging results for treatment decisions than stroke. Current guidelines mandate that a radiology report for suspicion of stroke be available within 25 to 30 minutes of patient arrival at the institution. As a stroke center of excellence, MU aims to consistently meet these guidelines. However, the complexities of the condition itself and of the system of care suggest that variation will occur. The purpose of our project is to assess the suitability of informatics tools like process mining and statistical process control to study the efficiency of imaging in the stroke care, to assess variability and its sources and to facilitate interventions to increase efficiency and reduce variability.