BIDS-BCHSI Research Xchange Forum — Holistic Clinical Care Platforms
Date: Tuesday, April 6, 2021
Time: 12:30-1:30 PM Pacific Time
Location: Virtual Participation
Register to recieve the virtual access link for this event.
12:30-12:50 PM — Socioeconomic Risk Screening, Documentation, and Interventions
Ben Lacar, 2020-2022 BIDS-BCHSI I4H Data Science Health Innovation Fellow
Abstract: Social determinants of health (SDOH) are conditions of the environments of people that affect a wide range of health, functioning, and quality-of-life outcomes. Despite the recent recognition that social adversity can negatively affect health, patient-level screening for socioeconomic adversity or protective factors has not gained widespread adoption. Barriers to screening include logistical barriers (e.g. limited time with patient, constraints of electronic health records) and relational barriers (e.g. lack of trust). Furthermore, patients and providers may be reluctant to discuss socioeconomic conditions in the absence of meaningful interventions. I seek to address tools that can support socioeconomic risk screening, documentation, and related interventions.
12:50-01:10 PM — Situational Awareness Support Dashboard for Clinicians
Akram Bayat, 2020-2022 BIDS-BCHSI I4H Data Science Health Innovation Fellow
Abstract: Situational awareness plays an important role in healthcare when clinicians make crucial operational decisions that have direct impact on patient outcomes. Situational awareness ensures that the right information is aggregated, comprehended, and projected correctly at the right time, but this is especially challenging as the volume of healthcare data increases and a clinician must quickly assess a patient’s medical history by searching for information that is scattered across multiple screens in the EHR. Our porosed Situational Awareness Support Dashboard (SASD) brings disparate data streams into a common operational display for real-time data-driven insights and actionable intelligence. SASD launches from a patient’s EHR encounter and delivers patient information into a dashboard with one click. SASD helps clinicians think thoughtfully and adapt a holistic view of the patient's health trajectory to ensure optimal treatment plans especially for complex patients such as patients with Multiple Chronic Conditions.
01:10-01:30 PM — Q&A
The BIDS-BCHSI Research Xchange Forum is an open discussion platform for the interdisciplinary exchange of ideas and research projects at the intersection of healthcare and data science. Participants are invited to engage in a variety of activities, including presentations of work-in-progress, discussions and critiques of recent papers and AI methods in healthcare, introductions to new tools and methods, and opportunities to foster new collaborations. Invited speakers include leading voices in AI and Healthcare, and active conversations invite participants to share fresh perspectives. Clinicians and physicians with an interest in data science methods and tools, as well as data science faculty and researchers with applications or interests in the healthcare and health sciences, are welcome and encouraged to participate. Regular participants will also include the I4H Fellows, as well as post-docs, staff, and faculty from UC Berkeley, UCSF, and Johnson & Johnson. The immediate goals of this Forum are to share our current research projects with a wider audience, and to increase engagement and improve communication among the three host organizations. Meetings are held virtually on the first Tuesday of each month at 12:30-1:30 PM Pacific Time, and interested members of the UC Berkeley, UCSF, and Johnson & Johnson communities are invited to sign up for this group's mailing list to receive information about upcoming webinars. Please contact InnovateForHealth@berkeley.edu for more information.
Dr. Benjamin Lacar studied biochemistry at UCLA and earned his Ph.D. in neuroscience from Yale University. His postdoctoral research at the Salk Institute was the first to report transcriptomes of activated single-neurons. He subsequently joined Fluidigm to help develop genomics applications. In his initial role as a product applications scientist, he analyzed data for marketing, educated staff and customers, and supported external collaborations across a variety of research domains. He then created analysis pipelines to facilitate method development as a bioinformatics scientist. Seeking to extend his data science skills towards societal impact, he completed an Insight Data Science Fellowship where he identified schools and community features associated with low-income student success using binary classification models. As an Innovate For Health Fellow, he is interested in impacting health care, especially for underserved communities. He looks forward to discussing innovative approaches with the faculty, staff and fellows in the program.
Dr. Akram Bayat is a data scientist with expertise at the intersection of AI and healthcare. She was a postdoc associate at MIT Media Lab where her research focused on creating novel intersections between engineering, medical imaging, machine learning, and medicine to develop innovative high-impact patient-centered research. Akram received her Ph.D. in computer science from the University of Massachusetts Boston. During her Ph.D. research, she worked on developing machine learning algorithms for solving real-world problems and conducted experimental studies for modeling of human physical and behavioral characteristics. Akram has significant expertise in applying deep learning for computer vision applications. Her research has been published in leading computer science conferences and journals and won numerous awards. Her recent work in computational staining of pathology images was published at Jama and mentioned at MIT News in May 2020.