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The COVID-19 pandemic has prompted a surge in demand for telehealth services, but many questions about how healthcare providers can adapt their practice to meet the challenges of telemedicine remain to be answered. Now, a group of rheumatologists at Duke University School of Medicine have used data drawn from the Duke University Health System’s EHRs (electronic health records) to investigate how a rapid transition to telemedicine affected their approach to patient care.

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In this one-hour virtual learning experience, 3 teams of Duke investigators will discuss their proposal concepts with data science experts. For April 5, proposal concepts will include genomic analysis related to sickle cell anemia, lifestyle intervention adherence, and transplant optimization. The proposal studio vLE concept is newly launching in spring 2021, with the goal of assisting Duke investigators with proposal development in health data science, and in sharing experiences with the broader Duke community. The series is co-hosted by Duke AI Health and the Duke+Data Science (+DS) program.

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AI Health is currently considering requests for placement of a Microsoft-Duke AI Health Data Science Fellow for projects proposed by Departments/Divisions within the Duke University School of Medicine. The Microsoft–Duke AI Health Data Science Fellowship is a 2-year training program in data science with direct application for healthcare. Funded in part by a grant from the Microsoft Corporation, Microsoft-Duke AI Health Fellows will also receive support from AI Health, the clinical divisions to whose projects they are assigned, and the Duke Department of Biostatistics and Bioinformatics, with overall program supervision provided by the Duke Clinical Research Institute’s Center for Predictive Medicine.

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The mission of Duke AI Health is to enable discovery, development, and  implementation of artificial intelligence (AI) at Duke and beyond. A key  component to achieving this goal is to foster high-impact, rigorous, and  competitive proposals for scientific awards. The AI Health Proposal Studios will provide a structured opportunity for investigators to engage with Duke’s top data science expertise and thought leadership, and to receive review and feedback of the scientific components of their proposals. The deadline for submitting applications is 5:00 PM Eastern time on Monday, December 7, 2020.

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A Machine Learning for Mobile Health workshop, part of the upcoming Neural Information Processing Systems Conference (NeurIPS 2020), is inviting contributions and extended abstracts from researchers and clinicians in the interdisciplinary machine learning and mobile health space, with the goal to better address the various challenges currently facing the widespread use of mobile health technologies in health and healthcare. Co-organized by Duke Statistical Science assistant professor Katherine Heller, PhD who is also a research scientist at Google AI, the workshop aims to facilitate collaboration between machine learning researchers, statisticians, mobile sensing researchers, human-computer interaction researchers, and clinicians from around the world.

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Seven Duke +DS learning experiences will be held in September. These sessions offer the opportunity to dive deeper into topics and target diverse units at Duke: from those that desire a broad understanding of what is possible with data science, and those who wish to use data-science tools (software) without a need for deep understanding of underlying methodology, to those who desire a rigorous technical proficiency of the details and methodology of data science. Anyone in the Duke community is welcome to join, there is no fee to attend, and no prior experience is necessary.

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Across the world and in the United States, multiple studies have shown that social distancing is effective at reducing the spread of SARS-CoV-2 both at interpersonal and statewide levels. An early analysis of social distancing in the United States amid the COVID-19 pandemic, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge by Duke undergraduates Shannon Houser and Jack Lichtenstein, echoed those findings and won the “Best Visualizations” prize at the contest. Using data available from Google Mobility Reports, the duo explored how factors such as population density, initial number of positive coronavirus cases per capita, governor’s political affiliation, and official shelter-in-place orders influenced the magnitude of a state’s social distancing early during the COVID-19 pandemic.

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Aside from altering the very fabric of daily life across the United States and the world, the COVID-19 pandemic has exposed the many existing shortcomings and inequities of the American healthcare system. The burgeoning public health crisis has resulted in more than 5 million confirmed cases nationwide and close to 163,000 deaths as of the beginning of August. However, some communities and groups have been disproportionately impacted, as a prize-winning analysis by Duke’s Meredith Brown, Matt Feder, and Pouya Mohammadi, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge.

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