Big Data, Regulation, and the Future of Health 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.

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.

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 of researchers past by training AI models to with image data from darker-skinned patients and the presentations of their symptoms.

With smart phones and access to technology increasing, the gaps in access to digital health will shrink in the coming years. 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. Informatics studies on health equity will benefit from increased standards, policies, and data addressing equitable care.

Concept Map

My concept map on Systemic Racism in U.S. healthcare.

I created this concept map first by hand drawing on a piece of paper and then digitizing it using LucidChart. Systemic racism has both causes and impacts in society at large, as well as specifically in healthcare. To be accurate, I felt strongly that I must represent both aspects in the concept map. First, I focused on the overall concept of systemic racism, and then I added in the healthcare-related impacts to my map. Topics included are health disparities, telehealth, access to care, Medicare and Medicaid, social determinants of health, preventive care, clinical research, and precision medicine (big data). I did not include HIEs, security, consumer health, human factors, or EBP and PBE in my concept map.

Throughout this course, I learned three main things. First, racial disparities are huge in healthcare outcomes. Second, healthcare informatics is a very complex but much-needed field. Third, data science will be vital to the future of healthcare in order to derive meaning from the massive amounts of data being collected through information systems. I hope to develop skillsets in data science, which I would love to apply to improving health equity, as well as bigger picture health informatics challenges.

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