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Putting a Spotlight on Disparities in Care: An Important Piece of the Health Equity Puzzle

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We are living in a time of unprecedented awareness of the value of diversity, equity, and inclusion across every facet of U.S. culture. And in healthcare, new insights are constantly emerging into the depth and prevalence of racial disparities. Yet, despite the growing recognition, limited progress has been made in the healthcare industry to meaningfully address these issues. Although disparities are well documented, especially in the US, we have historically lacked visibility into timely patient-level differences in care that would best position us to act.

It is often said in business that you can’t change what you don’t measure. The same is true for disparities in care. If we don’t measure these inequalities, how can we ever hope to address them? 

Measuring, and ultimately addressing, care disparities facing patients of color requires a deeper view into real-world patient data and the insights captured therein. The inclusion of expanded demographic data with the patient journeys captured in our Healthcare MapTM gives us the opportunity to better assess the up-to-date, real-world experiences of diverse patient populations. The insights found in this data supports our customers and partners in shaping solutions to improve health equity, and provides the evidence needed by policymakers and health agencies in their initiatives for equity.

What gets measured, gets managed: Diversity in clinical trials 

One key area where we see significant opportunities for data to improve racial and ethnic representation in the medical system is in clinical trial development and deployment. Diversity has historically been overlooked or unprioritized in clinical trial design and implementation, and patients of color are consistently underrepresented. A 2018 analysis in Nature found that, in 2014, 86% of participants in drug trials were White, having dropped just six percentage points in the previous 17 years. This trend is echoed in recent findings from Komodo Health, which found that 85% of oncology trial participants over the last five years were White, while Black patient representation remained stagnant at 6.7%.

Increased diversity in clinical trials is essential to ensure our medical treatments and therapies work for the whole population. One way to improve outcomes for patients of color is to change the model used to design and recruit for trials, and how we measure successful research initiatives. With a newfound capacity to identify diverse patients that meet specific disease criteria through a more complete dataset, Life Sciences organizations will be able to capture more of the spectrum of the differences affecting how treatments work, and prevent already marginalized groups from becoming even more disproportionately burdened. We’ll also be able to revisit some of medicine’s core assumptions that have been based on a racially homogenous population. 

Data and technology can drive more equity in clinical trials, for example by unlocking more opportunities for decentralized trials. However, decentralization alone does not foster diversity. We know that technology can also introduce new barriers for underserved communities who might otherwise benefit from the opportunity to participate in a trial remotely. And, even when trials occur in treatment centers with more diverse populations, patients of color are less likely to be enrolled. 

The movement towards health equity will require action from all sides. Diversity in clinical trials must be paired with efforts to expand and deepen our understanding of social determinants of health. Staff diversity in healthcare, as well as in health research organizations, must be addressed to increase representation and tackle embedded structural racism. Our datasets and the AI they power must be monitored for racial biases that only grow as technologically-sourced insights are increasingly relied upon to inform solutions. 

An imperfect tool 

It's also important to acknowledge that these metrics are far from perfect. It is well accepted that  race and ethnicity categories are nonbiological, indistinct social constructs without genetic basis. They are typically weak proxies for genetic realities, and can also be weak proxies for social determinants of health. Crude racial and ethnic segmentation can also drive, opacify or exacerbate racist systems. Clinical predictions based on current racial categories have high error rates, and race-based assumptions can be problematic in a medical setting, where they can underlie clinical protocols that compound disparity. Lab tests for kidney function are a prime example of this issue.

To have a complete view of a patient, we must have integrated, current, linked data about the entire individual, their experience in the healthcare system and social determinants of health. Only then we can begin to get a clearer view of the actions needed to reduce or eliminate disparities. 

Complex problems demand multifaceted solutions

It’s likely that in time we will develop and validate variables that do a better job of pointing to differences in health. But even in perpetuity, our evolving racial categories may continue to be an important variable for understanding certain influences on health. 

As we advance in our journey to build the most complete view of the patient, the incorporation of expanded race & ethnicity data unlocks a newfound capacity to bring to light race-based disparities in healthcare, patient journeys, and outcomes.

Patient-level healthcare experiences paired with race and ethnicity data gives us a new power of perspective, and it’s in understanding the nuances of these issues that we are able to craft the most appropriate solutions. 

Learn more about data-driven innovation in clinical development in "The Future of Clinical Trials Is Anything but Random." 

To see more articles like this, follow Komodo Health on Twitter and LinkedIn, and visit Insights on our website.

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