Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Abstract

<p class="Default">Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged popula­tions over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most impor­tantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention devel­opment. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health dis­parities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for re­ducing health disparities but without active engagement, it risks further widening them.</p><p class="Default"><em>Ethn.Dis;</em>2017;27(2):95-106; doi:10.18865/ed.27.2.95.</p>

The Research Centers in Minority Institutions (RCMI) Translational Research Network: Building and Sustaining Capacity for Multi-Site Basic Biomedical, Clinical and Behavioral Research

“…32 RTRN is coordinating efforts to leverage these population health resources by deploying informatics tools such as i2b2, 9 that will support data science initiatives, and accelerate research on the science of health disparities. 33…”

Artificial Intelligence for Good Health: A Scoping Review of the Ethics Literature

“…While there is increasing support from funding bodies for research on the social and ethical implications of AI [14][15][16][17], to date there has been limited attention by the ethics research community on AI within the field of health. The health sector, however, is a growing area of AI research, development and deployment, with AI holding promise for the promotion of healthy behaviours; the detection and early intervention of infectious illnesses and environmental health threats; and the prevention, diagnosis, and treatment of disease [18][19][20]. These and other AI applications have the potential to aid in the realization of the United Nations Sustainable Development Goals (SDGs), including SDG 3.3 [21] to achieve universal health coverage, and SDG 10 [22] to reduce inequality within and between countries.…”

Translational Health Disparities Research in a Data-Rich World

“…The NHANES accelerometer data described above, with its rich array of socioeconomic identifiers, also could be used to identify disparities and to test disparity hypotheses related to nutrition and health, including reliability between big data and self-reports. Although only few studies have successfully used big data sources to ad-vance health disparity research, 18 data mining and machine learning (ML), coupled with advances in hardware technology, signal more opportunities for using big data in translational health disparity research.…”