Big Data Neglects Populations Most in Need of Medical and Public Health Research and Interventions

Big data should be extremely useful to medical and public health professionals attempting to engage in research, intervention, and precision medicine. The use of big data in these areas, however, has the potential to ignore a large portion of the population, because the sources of much of the data – social media, wearables, electronic health records, and insurance claims – are not utilized by that subset. These omitted populations, such as minorities and low-income individuals, are at a greater risk for health disparities and are the very populations that could most benefit from research and intervention. This paper argues that the scope of big data and the subsequent health uses of the information gathered needs to be broadened to increase the diversity of the data, especially by including those most vulnerable to diminished health outcomes. Building on existing government measures such as the Lifeline program to provide internet access to the underprivileged and NHS guidelines for the inclusion of minorities in research, we propose monetary, programmatic, and regulatory recommendations as means of addressing, and ultimately remedying, this problem.

[1]  W. Raub From the National Institutes of Health. , 1990, JAMA.

[2]  D. Greenwood,et al.  The use of supermarket till receipts to determine the fat and energy intake in a UK population , 2001, Public Health Nutrition.

[3]  E. Kinney,et al.  Health Insurance Coverage in the United States , 2002 .

[4]  P. Lawson,et al.  Federal Communications Commission , 2004, Bell Labs Technical Journal.

[5]  J. Kaye,et al.  Lessons from European population genetic databases: comparing the law in Estonia, Iceland, Sweden and the United Kingdom. , 2005, European journal of health law.

[6]  L. Egede,et al.  Epidemiology of type 2 diabetes: focus on ethnic minorities. , 2005, The Medical clinics of North America.

[7]  L. Pray,et al.  Challenges for the FDA : the future of drug safety : workshop summary , 2007 .

[8]  B. Kennedy,et al.  African Americans and their distrust of the health care system: healthcare for diverse populations. , 2007, Journal of cultural diversity.

[9]  D. M. Grant,et al.  The Art and Science of Personalized Medicine , 2007, Clinical pharmacology and therapeutics.

[10]  H. Myers,et al.  Race, racism and health: disparities, mechanisms, and interventions , 2009, Journal of Behavioral Medicine.

[11]  T. Bodenheimer,et al.  Confronting the growing burden of chronic disease: can the U.S. health care workforce do the job? , 2009, Health affairs.

[12]  David R Williams,et al.  Socioeconomic disparities in health in the United States: what the patterns tell us. , 2010, American journal of public health.

[13]  K. Arriola,et al.  Distrust in the Healthcare System and Organ Donation Intentions Among African Americans , 2012, Journal of Community Health.

[14]  R. Hasnain-Wynia,et al.  Disparities in Enrollment and Use of an Electronic Patient Portal , 2011, Journal of General Internal Medicine.

[15]  Ara Darzi,et al.  Preparing for precision medicine. , 2012, The New England journal of medicine.

[16]  C. Carter,et al.  Dialogues on diversifying clinical trials: successful strategies for engaging women and minorities in clinical trials. , 2012, Journal of women's health.

[17]  Report on Status of Regulatory Science at FDA , 2009, Pharmaceutical Medicine.

[18]  Jill P. Mesirov,et al.  Criteria for the use of omics-based predictors in clinical trials , 2013, Nature.

[19]  Fabricio F Costa,et al.  Social networks, web-based tools and diseases: implications for biomedical research. , 2013, Drug discovery today.

[20]  Hsien-Chang Lin,et al.  Use of electronic medical records differs by specialty and office settings. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[21]  J. Henry,et al.  Adoption of Electronic Health Record Systems among U . S . Non-Federal Acute Care Hospitals : 2008-2015 , 2013 .

[22]  Jonas Lerman,et al.  Big Data and Its Exclusions , 2013 .

[23]  K. Greiner,et al.  Moving forward: breaking the cycle of mistrust between American Indians and researchers. , 2013, American journal of public health.

[24]  Fabrício F. Costa Big data in biomedicine. , 2014, Drug discovery today.

[25]  Michelle Dunn,et al.  The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data , 2014, J. Am. Medical Informatics Assoc..

[26]  L. Ohno-Machado,et al.  “Big Data” and the Electronic Health Record , 2014, Yearbook of Medical Informatics.

[27]  J. Thorpe,et al.  Big Data and Public Health: Navigating Privacy Laws to Maximize Potential , 2015, Public health reports.

[28]  Alessandro Mantelero,et al.  Data protection in a big data society. Ideas for a future regulation , 2015, Digit. Investig..

[29]  Dominic Coey The effect of Medicaid on health care consumption of young adults. , 2015, Health economics.

[30]  Albert Y. Zomaya,et al.  Big Data Privacy in the Internet of Things Era , 2014, IT Professional.

[31]  B. Najafi,et al.  Precision Medicine: A Wider Definition , 2015, Journal of the American Geriatrics Society.

[32]  Eberechukwu Onukwugha,et al.  Big Data and Its Role in Health Economics and Outcomes Research: A Collection of Perspectives on Data Sources, Measurement, and Analysis , 2016, PharmacoEconomics.

[33]  Jessica C Smith,et al.  Health Insurance Coverage in the United States: 2014 , 2015 .

[34]  D. Longo,et al.  Precision medicine--personalized, problematic, and promising. , 2015, The New England journal of medicine.

[35]  Makram Talih,et al.  Measurement of Health Disparities, Health Inequities, and Social Determinants of Health to Support the Advancement of Health Equity. , 2016, Journal of public health management and practice : JPHMP.

[36]  E. Hing,et al.  Adoption of Certified Electronic Health Record Systems and Electronic Information Sharing in Physician Offices: United States, 2013 and 2014. , 2016, NCHS data brief.

[37]  Charles F. Hofacker,et al.  Big Data and consumer behavior: imminent opportunities , 2016 .

[38]  Markus Hammer,et al.  How big data can improve manufacturing , 2022 .