Fairness in Precision Medicine is the first report to deeply examine the potential for biased and discriminatory outcomes in the emerging field of precision medicine; “the effort to collect, integrate and analyze multiple sources of data in order to develop individualized insights about health and disease.”
Fairness in Precision Medicine is the first report to deeply examine the potential for biased and discriminatory outcomes in the emerging field of “precision medicine,” or “the effort to collect, integrate, and analyze multiple sources of data in order to develop individualized insights about health and disease.” Supported by the Robert Wood Johnson Foundation, the report is the first in a new series of research projects at Data & Society focused on the future of health data.
The authors–Data & Society Postdoctoral Scholar Dr. Kadija Ferryman and Data & Society Researcher Mikaela Pitcan–present insights on emergent tensions in the field arising from extensive qualitative interviews with biomedical researchers, bioethicists, technologists, and patient advocates.
Among the report’s key findings is a potential for bias and discrimination both in datasets (through a lack of cohort diversity; technical processes of data collection and cleaning; or the specific incorporation of electronic health record data) and in outcomes (through too much focus on individual responsibility for health; or the marginalization of population groups with lower health literacy or in less resourced areas).
Fairness in Precision Medicine clears a path for possible technical, organizational, and policy-oriented remedies. Among the report’s recommendations is that precision medicine researchers “recruit diverse participant pools in order to address the historical lack of representation in medical research” and “involve participants and patients as active participants in medical research.”
Ferryman and Pitcan also suggest that practitioners in both biomedical research and clinical practice settings incorporate a practice of diversity by considering additional factors, such as geographic and socioeconomic diversity and continental ancestry, when collecting health data. Additionally, the authors emphasize that while data security is important, “keeping data private and secure will not assure that these data will not be misused.”