In this final event of the Deinard Lecture Series, four panelists will discuss the Internet, Big Data, and the Precision Medicine Initiative and the questions they raise about medical research, privacy, data ownership, and ethics. A robust discussion and question-and-answer period will follow.
The Consortium conducts original research, serves students and faculty, and advances public dialogue and understanding on emerging issues at the intersection of science and society.
We are pleased to announce a funding opportunity for graduate and professional students on all University of Minnesota campuses. Each year, the Consortium provides funding for intramural projects related to the societal implications of problems in health, environment, and the life sciences. Grants will be awarded in the spring 2017 for work during the summer 2017 and academic year 2017-18. Student organizations may apply for these grants. A total of $35,000 is available with a maximum individual award of $7,000. Awards can include a stipend for research and writing and funds for research supplies, or funding for a program or colloquia. This funding initiative aims to encourage work on the broad societal implications of problems in health, environment, or the life sciences. Proposals for student-initiated programs or colloquia will also be accepted. The Request for Proposals (RFP) and application materials may be found here. Deadline for submission is Monday, February 13, 2017. Awards will be announced by the end of March. If you have questions, please contact Audrey Boyle at firstname.lastname@example.org or 612-626-5624.
On Tuesday, Dec. 6, the Consortium will be hosting our final event in the Deinard Memorial Lecture series on Law & Medicine, "How Patients Are Creating Medicine’s Future: From Citizen Science to Precision Medicine." Join four leading thinkers – Ernesto Ramirez, PhD (Fitabase), Barbara Evans, PhD, JD, LLM (University of Houston Law Center), Kingshuk Sinha, PhD (Carlson School of Management, University of Minnesota) and Jason Bobe, MSc (Icahn Institute for Genomics and Multiscale Biology) – for a lively discussion of this emerging area. Panelists will explore how the Internet and Big Data are transforming science; fitness trackers and wearables are streamlining the gathering of health data; and the Precision Medicine Initiative seeks to harness these data together with genomics to create personalized medicine. Meanwhile, patient groups want an active role in the development of therapies and drugs. A robust discussion and question-and-answer period will follow. This event is free and open to the public; box lunch is provided for registrants. Register here.
In the early 1980s, during the initial throes of the AIDS epidemic, a flight attendant named Gaetan Dugas came to be identified as "Patient Zero" because he was represented in popular culture as the person who brought HIV to North America. A recent study published in Nature used genomic data to map the spread of HIV during that time, demonstrating conclusively that Dugas was not the North American index case as previously depicted. An analysis of the case by Greg Clinton explores "the spectacle of disease narratives, not only what they emphasize, but what they tend to obscure." Drawing on the groundbreaking work of Priscilla Wald and others, Clinton describes how epidemiological narratives, most famously that of Typhoid Mary, are "typically bound up with literary concerns, such as the assignment of 'hero' and 'villain' status to a person or group." He argues for consumers to apply "critical consciousness" to such media-driven spectacles, resisting the all-too-human temptation to passively absorb narratives that assign meaning, "even if that meaning is false and only serves to perpetuate fear of the Other." Read the entire article here.
"Would you trust an algorithm to help you with a medical diagnosis?" This question is posed by Christina Farr of Fast Company in her discussion of a collaboration between University of California, San Francisco (UCSF) and General Electric with the goal of finding out what Big Data approaches to diagnosis can – and can't – accomplish. The two organizations will be partnering for the next three years to "develop a set of algorithms to help radiologists distinguish between a normal result and one that requires further attention." Knowing the medical community will be skeptical about such machine-learning approaches, not to mention the lack of appropriate regulation for a diagnosis by a non-human, Michael Blum of UCSF notes, "There is a lot of concern from the public and from clinicians that we’ll be developing things to replace doctors. These developments will be focused on supporting clinicians and in developing safer workflows." Read the entire article here.