' Big Data: Transitioning Away From the White Male Norm | MTTLR

Big Data: Transitioning Away From the White Male Norm

As the capacity to generate and use digital information increases, the use of big data has permeated many industries. Its usage in medicine is poised to make major impacts on clinical practice. There are many benefits to the quality and efficiency of healthcare that can be achieved through the utilization of big health data. But there is a need for an understanding of how big data will affect populations that face disparities and inequalities in medicine – women and people of color.

In medicine, the white male is generally the default. This default often affects how women and people of color are diagnosed and treated. Women may go undiagnosed and untreated due to having “exclusively female disease” or diseases that occur more frequently in women than men. Or they are misdiagnosed because their symptoms don’t manifest in the same way they do in men. For people of color, differences in race may affect the efficacy of drugs and medical devices. For both populations, they may ultimately have to be sicker or wait longer to qualify for the same treatment as a white man.

Big data may help overcome these disparities through recognition of patterns in the treatment of women and people of color. Data generated during the course of care can be used to measure quality, develop hypotheses, and compare effectiveness of different treatments. Artificial intelligence (AI) technology provides the ability to take massive data sets and find patterns. These identified patterns may reveal gender and racial differences that affect diagnosis and treatment. Through the use of big data in precision medicine, for example, the identification of “biological variation among patients and correlate[ion of] that variation to differences in the most effective and efficient treatment” can occur. The onset of symptoms of a heart attack in women that present differently from those in men and inadequately calibrated oxygen intervention thresholds for African Americans are examples of documented patterns that big data and AI probably might have recognized quicker and at less expense.

Big data may also help healthcare providers spot issues with drugs and medical devices that go unnoticed due to inadequacies in the clinical trial process. Women and people of color are underrepresented in clinical trials. Collection of data and analysis of the effects of drugs and medical devices in clinical practice may prevent certain provisions of care from becoming the standard for adversely affected populations. Health data, which can be disaggregated by sex and race, can be used in comparative effectiveness research to identify drugs and medical devices that don’t work well for women and people of color. Data on the clinical treatment of women and people of color may also be used to study the side effects, off-label use, and surrogate endpoints.

While big data can be used to overcome disparities in healthcare caused by gender and racial differences, there are obstacles. One issue is the lack of data. There exists a “data gap” in research and medical knowledge regarding women and people of color. This data gap could be due to a variety of reasons, including the lack of representation in clinical trials, and from the medical field’s failure to collect such data because of its inability to recognize the need for it.

The gap is also affected by a lack of access to technology and healthcare which disproportionately affects women and people of color. Data needed to implement big-data analytics comes from electronic health records, insurance claims, devices such as smartphones and fitness trackers, social media, etc. Only 67% of the global population has a cellphone and 51% do not have access to mobile Internet. While accessibility to cellphones and mobile Internet may be higher within the U.S., there is still a lack of access to these technologies within low socioeconomic populations, including many women and people of color. Additionally, lack of health insurance and access to healthcare means databases and electronic health records are missing data on underrepresented populations. Without large sets of healthcare data from women and people of color, AIs cannot be trained to recognize gender and racial differences.

Lack of trust further exacerbates the issue of data gaps for women and people of color. Past mistreatment and current fears create mistrust within certain populations regarding use of their health data. Instances of injustice like the use of Henrietta Lacks’ cancer cells without consent or the Tuskegee syphilis experiments foster this mistrust. The potential for disease surveillance without consent or concern for privacy rights during the current pandemic and the concern for continued surveillance after the end of the pandemic may stoke fears about health data usage. Media attention and unfamiliarity with big data and AI also contribute to these fears.

There is also potential injury from privacy violations, including discrimination, embarrassment, and other “dignitary harms” that may cause mistrust. Discrimination resulting from privacy violations may disproportionately affect women and people of color who already face increased discrimination. With the present ability to re-identify de-identified data, this lack of trust is even more understandable. This lack of trust and the resulting unwillingness to consent to usage of their data is thus a limiting factor to the use of big data to overcome racial and gender disparities in medicine.

Universal healthcare is a potential solution. Access to healthcare for more women and people of color will increase the availability of data. But there would still be a need to overcome the lack of trust regarding the use of health data. There are a variety of ways to foster trust: including representative patients in the decision-making process on data usage, data trusteeships, “consumer-driven data commons”, “citizen juries”, etc. Regulation that is both stronger and clearer than current privacy laws could also calm some mistrust regarding data privacy. If these effectively encourage more women and people of color to share their big health data, medicine could see a shift away from the white male norm to differing standards of care that recognize gender and racial differences.

* Kimberly Parry is an Executive Editor on the Michigan Technology Law Review.

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