Big Data, Regulation, and the Future of Health Equity

Discussing Health Equity: A Starting Point

If you search for statistics on health equity or disparities in the U.S., you will find a plethora of studies and statistics providing examples of inequality in health outcomes for racial and other minority groups. When thinking about how to resolve these inequities, it’s important to understand the context driving them. My concept map below on Systemic Racism in U.S. Healthcare provides a high-level breakdown of why inequities exist in the health of the American population. Systemic racism has both causes and impacts in society at large, as well as specifically in healthcare. Both of these aspects are represented in this concept map.

The State of Regulation and Ethics in Health Equity

When it comes to regulating healthcare equity, there are some regulations in place by the federal government. However, as shown by the vast disparities in healthcare statistics today, the annual health equity acts have not gone far enough. The Health Equity and Accountability Act of 20201, introduced in Congress on April 28, 2020, could lead to some improvements, such as the establishment of formalized data standards for collecting racial data in healthcare settings.

Rep. Adam Schiff championed the cause by introducing another bill on September 29, 2020, called the Equal Health Care for All Act2. In the new bill, Schiff specifically calls to treat equitable care as a civil rights issue. The legislation would result in increased funding and data related to equitable care, in addition to formalizing the process for investigating patient complaints of inequitable care.

Informatics studies on health equity will benefit from increased standards, policies, and data addressing equitable care. Since health is cultivate outside hospital walls in our daily lives, consumer technologies also need to consider equity.

Health Equity and the Consumer Experience

One of the main health equity challenges related to consumer informatics is health literacy. Often, patients and caregivers are assumed to have better understanding of health and healthcare than they do. Patients need to be equipped with basic health literacy in order to be effective partners to their physicians in managing their healthcare. Furthermore, language barriers can pose further difficulty in developing health literacy. These challenges can be addressed through consumer informatics tools that facilitate patient education and care management. Many technology platforms also provide translation options, which could help immigrants to better understand the terminology used in health settings. Consumer informatics can also play a role in helping families with low access to care learn about opportunities for free screenings, check-ups, and other health services.

In order for consumer informatics tools to be effective in improving health equity, they must be accessible by the populations they are intended to help. As advised by Dr. Robin Jacobs in her article “Using Consumer Health Informatics to Address Health Disparities,”1 research and testing need to be done to ensure that the most disadvantaged populations are both willing and able to use the consumer informatics tools and which devices they prefer. This will ensure that informatics interventions will be successful. Some of these interventions could include patient apps or portals that provide patient education content in multiple languages, translator apps, or social media pages that offer health literacy education.

With smart phones and access to technology increasing, the gaps in access to digital health will shrink in the coming years. The resulting challenge will be using the vast amount of healthcare data collected by these apps and other technologies in an ethical and equitable way.

Looking Ahead…Health Equity in the Age of AI

In the next 5-10 years, AI will be a double-edged sword in terms of equity. The next leaps in medicine will come from Big Data – but patients have to give consent for their data to be shared. While populations are already very risk-averse when it comes to giving companies access to their health data, Black patients may be even more unwilling due to overall (and understandable) distrust of the healthcare field. Furthermore, the volumes of patients in each racial/ethnic category are not the same across all groups. While Big Data presents an opportunity to take diagnosis, prevention, and treatment quality to the next level, the benefits of these technologies may be greater for White patients, who have more data included in studies using Big Data. Black and other patients of color are already underrepresented in clinical trials and other medical research. Even though the atmosphere of studies are shifting, the inequities could still remain.

On the flip side, there is opportunity for Big Data to be an equalizer if utilized safely, carefully, and with good intentions. If enough patients of color provide data to these data analyses, the advances outside of the inequitable clinical research field could help to bridge the existing research equity gap. For example, current medical textbooks have been criticized for using only examples of patients with fair-colored skin to train doctors to recognize symptoms. This leaves a knowledge gap in identifying the same condition in patients with darker skin, where symptoms like rashes or redness may appear differently. Image recognition and machine learning could help to rectify the mistakes and neglect of researchers past by training AI models with image data from darker-skinned patients and the presentations of their symptoms.

As long as standards are created and enforced for ethically applying AI to shared patient data, the technological and access-based causes of health inequity could be resolved significantly. However, societal issues like education, housing, and economic stability will not be fixed through technology alone.

As the healthcare system moves away from paper-based medical records and into digital, awareness in health equity will grow. We are in a moment with strong societal pressure to heal America from the system racism that has plagued it since its founding days. More than ever before, there is a focus on bringing these calls for social justice to healthcare. The outcome remains unclear – will we apply the powers of technology to close the equity gaps or to reinforce our existing, unjust patterns?

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