The data collected from mobile mental health apps has promising benefit toward the field of mental health. The risks are usually limited to breach of confidentiality and investigators mitigate those risks by implementing processes to protect access to the data. In studies involving algorithms and personalized medicine, the risks increase. So, how do IRBs determine when risks outweigh the benefits? Read more
The digitization of everyday life has led to an interesting phenomenon for research administrators; the ethical concerns that arise from secondary uses of large and open data now pose a greater challenge for the ethical management of research data than do the conventional challenges of primary data acquisition. As debates over consent forms give way to discussions of differential privacy, it is hard to ignore the new reality that the highest levels of risk and benefit to human participants in research may now arise from secondary data uses. What should research administrators and IRB members do to understand and manage the risks and benefits? Read more
At PRIM&R's 2019 Advancing Ethical Research Conference, Ivor Pritchard, PhD, discussed the relation between public, private, and social information in his session, The Secrets of Big Data: Public, Private or What? (B15). Dr. Pritchard asked, what are the risks and confidentiality provisions for social information?
First, the concept of social information can be described as neither publicly nor privately available, but information provided in controlled settings. As an example, data gathered from a classroom or an online chatroom are neither public nor private in that the subject is a) not alone and b) not everyone has access to that environment. Then, what are the risks to subjects in social experiments (experiments conducted in public settings like [...] Read more
In April, the Food and Drug Administration (FDA) issued a discussion paper, "Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD).” The paper represents FDA’s response to the growing number of medical device manufacturers who are utilizing artificial intelligence and machine learning technologies to continuously improve their products. On June 3, PRIM&R submitted comments in response to the discussion paper, thanking the FDA for their consideration of the public health implications of the use of these technologies, but also cautioning that any new regulatory approach in this area must address the protection of individuals whose personal information and data are being used in the creation and ongoing testing of these technologies. Read more
data-rich networked information technologies are unique in that they represent people’s lives and activities, bridge multiple dimensions of a person’s life, and are often collected, aggregated, exchanged, and mined without them knowing. We call this data “pervasive data,” and the increased scale, scope, speed, and depth of pervasive data available to researchers require that we confront the ethical frameworks that guide such research activities. Read more
