Realizing the Promise of Precision Medicine for All Requires the Data of All

Imagine a world in which a personalized cure for each person’s sickness was possible. 

Sir Thomas More first introduced the idea of a “utopian world” in which he envisioned a “perfect” and “free of conflict” society void of all suffering: a world to be desired by all its citizens (British Library). This idea of suffering relates to a myriad of things, one of them being suffering from pain, which is what medicine is usually about. It also relates to suffering from societal injustices, which need to be overcome in order to alleviate suffering from pain. Evidently, we are far from such a society. Imagine a world, however, in which a personalized cure for each person’s sickness was possible. Precision medicine is an up-and-coming area of research that aims to bring such a world into existence globally by specifically taking into account individual differences such as genetics and social and economical factors to tailor health care to individual bodies and needs (Kadija and Pitcan, 2018).

Dr. Akinlolu Ojo, a global health researcher who focuses on minority health disparities among African American communities, states, “The whole idea of precision medicine is to use individual differences to tailor [the] prevention care that they receive” (Akinlolu reference). In order to make such advancements in health research, it is imperative that data scientists and medical researchers first resolve the disparities and biases existing in health data collection and use, one of which is insufficient health data of people of color. It is difficult to improve health outcomes without pulling healthcare data from a variety of diverse sources. Therefore, in order to enhance health outcomes by prioritizing the need for personalized treatments, equitable healthcare requires both ethical data collection practices and the inclusion of individuals from all backgrounds, especially those who come from marginalized communities and low socioeconomic backgrounds. Only then can the utopian promise of precision medicine be realized.

While the promise of precision medicine rests on the vision of care tailored to the unique qualities of the individual, the data that drives precision medicine initiatives comes from a concerningly small sample of the population. In a report evaluating data management for precision medicine, the eHealth Initiative has found that,

The people who participate in clinical trials tend to be more homogenous and are usually wealthy, Caucasian males in 50s and 60s. When drugs are developed based on small datasets from a relatively homogenous population, they turn out to be not as effective when applied to a more heterogeneous population in society (eHealth Initiative, 14).

Traditional medicine takes a “one-size-fits-all” approach, which is not successful for every individual because it does not take into account pertinent individual differences (Hopp, Li, and Wang, 1648). A homogenous dataset raises problems for precision medicine. The lack of health data of minority groups results in an inadequate representation of populations who are expected to benefit from the new medical techniques. This exclusion creates a large gap in decision-making and outcomes. Rather than promoting novel ways of improving health and treating diseases for these communities. Dr. Kadija Ferryman, a cultural anthropologist whose research focuses on the moral values and ethics surrounding digital health information and Mikaela Pitcan, a social scientist who studies how technology can affect decision-making in promoting prejudice, argue that researchers and clinicians should “recruit diverse participant tools in order to address the historical lack of representation of medical research” (Kadija and Pitcan, 2018: 10). Underrepresented communities, such as people of color, tend to suffer from more severe and rare illnesses because they do not have access to basic necessities such as clean water and healthy food. Lack of basic needs compromises immunity as well as increases susceptibility to toxic stress, which could potentially increase the likelihood of developing rare diseases. Excluding their data prevents precision medicine from tackling complex health problems.

The combination of heterogeneous big data and patient-specific lifestyle and environmental factors is the backbone of precision medicine and will allow for us to take a step forward in reaching a “utopian” society. 

The combination of heterogeneous big data and patient-specific lifestyle and environmental factors is the backbone of precision medicine and will allow for us to take a step forward in reaching a “utopian” society. Integration of a diverse data set is key to achieving the right treatment and ensuring that precision medicine attains its true potential, yet most of the data incorporated in research studies is homogenous. Dr. Edward Ramos, who investigates the influence of genes and societal factors on disease distribution, comments on this issue of representation in the All of Us research program, a United States National Institutes of Health precision medicine initiative. This program emphasizes how the practice of medicine can only be beneficial to a whole population if we understand the nuances in each individual’s disease, and a homogenous data set poses problems for precision medicine to move forward. Ramos argues, “The more diverse representation we have, the better outcomes we can give back to the public.”

There are a number of reasons, however, that make it difficult to gather data from ethnic communities. First, marginalized communities often do not have access to the latest and most expensive technology, such as the Apple Watches and Fitbits, which are used to gather health data and to predict health-related illnesses (Haskins). As a result, data on these populations is often missed. Dr. Jason Shafrin, a research economist at Precision Health Economics, explains that in order to find treatments to diseases based on individual needs, researchers should take into account each individual’s specific genes, environment, and lifestyle (Shafrin). The clinical trials should include people of a variety of backgrounds with different diseases, but researchers must first have access to such data. Second, and more importantly, many of these individuals reject efforts by researchers to collect their data; people at the margins fear the very real possibility that their health data will be misused.

A culture of respect for patient privacy is essential in order for people of color to be more inclined to share their data. 

A key example of why people of color do not trust health researchers is the story of an African American woman Henrietta Lacks, who died of cervical cancer in 1951. While undergoing treatment at Johns Hopkins Hospital, renowned cancer researcher Dr. George Gey took a sample of her cells without her informed consent, disregarding her medical privacy (Hopkins Medicine). This points to a fundamental bioethical issue: the lack of communication between cell/tissue donors and researchers. Although Henrietta Lacks' biomaterials paved the way for several medical breakthroughs worldwide, the practice of ensuring the protection and care of Lack's medical information was crucial (Hopkins Medicine). Cases like this discourage many marginalized communities from sharing their health information. Therefore, a culture of respect for patient privacy is essential in order for people of color to be more inclined to share their data. Scientists and practitioners alike need to build trust with communities of color.The focus of health researchers should be to to collect data from people of color and other marginalized groups in a way that puts consent and confidentiality first. In a Stanford review on how to build trust with communities of color, Adames and Chavez-Duenas explain how, “underrepresentation of communities of color in health-related programs results in programs that do not adequately meet the health needs of these communities, which can lead to adverse outcomes” (Adames and Chavez-Duenas, 2015). They emphasize how critical it is to be more self-aware of others’ cultures and to learn how they view and understand “health” differently. Health professionals should be mindful of how race and associated stereotypes/biases affect the participation of these communities in health initiatives.

Ensuring ethical health data collection is important because the goal of health research and medicine is to revolutionize development and alleviate sickness. However, without the data of those who are most vulnerable, we cannot make progress in this field. While this data promises to help the community in the long term, there is deep mistrust of the medical community among many members of underrepresented groups. To overcome this barrier, it is important to think about long term strategies to establish trust. Rather than going into a space and asking for health data, researchers need to team up with local community organizations that have established ties with the community. Health data cannot be utilized as a tool to make a change in the world, transform human life, or better the future of our society unless we incorporate the data of all communities in a respectful and responsible way.

Works Cited

Adames, Hector and Nayeli Chavez-Duenas. “Building Trust with Communities of Color.” Stanford Social Innovation Review, 21 May 2015, https://ssir.org/articles/entry/building_trust_with_communities_of_color#bio-footer. Accessed 1 May 2018.

Ferryman, Kadija, and Mikaela Pitcan. “Fairness in Precision Medicine.” Data Society , 26 February 2018, https://datasociety.net/research/fairness-precision-medicine/ . Accessed 9 March 2018.

Haskins, Caroline. “If Your Apple Watch Knows You’ll Get Diabetes, Who Can It Tell?” The Outline , 21 February 2018, https://theoutline.com/post/3467/everyone-can-hear-your-heart-beat. Accessed 1 March 2018.

Heintzelman, Carol. “The Tuskegee Syphilis Study and Its Implications for the 21st Century.” The New Social Worker , vol. 10, no. 4, 2003, http://www.socialworker.com/feature-articles/ethics-articles/The_Tuskegee_Syphilis_Study_and _Its_Implications_for_the_21st_Century/ . Accessed 30 April 2018.

Ojo, Akinlolu. “Why All of Us? Why Now?” YouTube, uploaded by All of Us Research Program, 6 June 2017, https://www.youtube.com/watch?time_continue=9&v=B7m5rNkDjHE . Accessed 9 March 2018.

Ramos, Edward. “Why All of Us? Why Now?” YouTube, uploaded by All of Us Research Program, 6 June 2017, https://www.youtube.com/watch?time_continue=9&v=B7m5rNkDjHE . Accessed 9 March 2018.

Reis, Steven. “Why All of Us? Why Now?” YouTube, uploaded by All of Us Research Program, 6 June 2017, https://www.youtube.com/watch?time_continue=9&v=B7m5rNkDjHE . Accessed 9 March 2018.

Shafrin, Jason. “What’s All the Fuss About Precision Medicine?” US News , 25 August 2017, https://health.usnews.com/health-care/for-better/articles/2017-08-25/whats-all-the-fuss-about-pr ecision-medicine . Accessed 9 March 2018.

“The Legacy of Henrietta Lacks.” Johns Hopkins Medicine , https://www.hopkinsmedicine.org/henriettalacks/upholding-the-highest-bioethical-standards.html Accessed 28 April 2018.

“Thomas More’s Utopia.” British Library , http://www.bl.uk/learning/timeline/item126618.html . Accessed 27 April 2018