The increased use of big data has shifted the way in which biomedical research is designed and implemented. This kind of research has begun to pursue opportunities afforded by big data by relying on large‐scale databases, multiplication of data sources, advanced storage capacity, and novel computational tools that allow for high‐velocity data analytics. Big data also enable researchers to draw health insights from data sources that are not strictly medical, such as data from wearable trackers, social media, and Internet searches that open new prospects to accelerate health‐related research and potentially elicit breakthroughs that will benefit patients. Currently, a large portion of health‐related research depends on big data, while the novelty of techniques and methods brought by big data research brings new challenges to institutional review boards (IRBs). It is unclear, however, if those entities should be the responsible oversight bodies for big data research and, if so, which criteria they should use.
According to a manuscript published in the September-October 2020 issue of the journal Ethics & Human Research, big data investigations shift the way biomedical researchers design and carry out their studies because their work departs from the traditional research model since it is largely exploratory rather than hypothesis driven. The methodological novelty of big data research models brings new challenges and questions to IRBs, including whether they are the bodies responsible for assessing these projects. Given current technologies, analytic methods, and regulations, IRBs cannot take their traditional review frameworks as given since big data research models might not fit within the traditional national review policies for the protection of human subjects. Health-related big data research also challenges IRBs in referring to existing safeguards for ethics research, such as informed consent, privacy, confidentiality, and minimal risk. A threefold consideration is involved. First, individuals whose data are used in research often are not sufficiently informed concerning the use of their data. Second, breaches in data privacy and confidentiality represent a major source of risk stemming from the informational richness of large data repositories, which makes them a primary target for actors outside the research domain. Third, correlations arising from health‐related big data analytics can be abused by various actors for unethical purposes, such as discriminating against applicants to health insurance services or jobs based on health risk indicators.
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