The U.S. recently reported a grim set of statistics as the nation passed the two-year mark since the initial surge of the pandemic. At the same time, we recognized the month of April as Minority Health Month.
The coincidental timing of these two milestones places a spotlight on the data that reminds us of the fact that Black/African American, Hispanic/Latino, American Indian and Alaska Native persons in the United States experienced higher rates of COVID-19-related hospitalization and death compared with non-Hispanic White populations.
Although data and knowledge of the disproportionate application and access to health amongst minority populations are well known, data regarding the impact of the virus on exacerbating health disparities wasn’t immediately available. Instead, the pandemic served as a stark reminder of our patchwork of disjointed systems and unreliable data which continue to hinder efforts to identify health disparities, understand the root causes, and direct resources appropriately.
The persistence of disparities in healthcare
In the early days of the pandemic, imagine if Federal and State governments had fully employed the predictive population health analytics available. By leveraging data systems that assess risks associated with race, ethnicity, or other personal determinants, such as zip code or employment status, public health professionals would have better understood which communities were likely to carry the heaviest burden of severe illness, hospitalization, or even death. From determining optimal locations for testing and vaccination sites and working with local communities to increase access to digital and virtual health options, to developing targeted communications strategies for building trust, data-driven decision making would have gone a long way to mitigate the pandemic’s disproportionate impact on vulnerable communities.
While the pandemic surely exacerbated health disparities for underserved communities, health and health care disparities are not new. They have been documented for decades and reflect longstanding structural and systematic inequalities.
Studies show the U.S. spends more per capita on healthcare than any country in the world yet ranks poorly on healthcare access and outcomes compared to other high-income countries. Demographic shifts, rapid unplanned urbanization, the globalization of unhealthy lifestyles, limited access, underserved delivery of care - as well as historical institutional bias (both implicit and explicit) are all contributing factors.
Data justice as a strategy in healthcare and technology
To help bridge the gap, CGI is proactively embedding the principles of data justice as a strategy to help our healthcare clients better understand and eliminate health disparities through equitable and ethical data practices.
At the same time, we recognize that data and social justice have always been interconnected — one contributes to the other, and not always for the best. Data bias has enormous consequences on healthcare and human lives. Inherent bias can be built into how data is collected, analyzed, interpreted, and distributed. While technological advancements such as Big Data, algorithms and artificial intelligence (AI) have taken organizations to new heights, their inherent nature presents enormous risks that can multiply biased and discriminatory effects. Biased data can hinder innovating meaningful actions, strategies, and measurable progress toward more equitable outcomes. Data scientists must practice responsible AI, adjusting for naturally occurring bias in data and prioritizing the careful review of models for biased cases to ensure that they are functioning as planned and the results are accurate.
As one of the world's largest providers of IT and business consulting solutions servicing healthcare organizations, we take proactive steps to collect and analyze data through a lens of justice, equity, and ethics when developing innovative solutions for clients. These steps include:
- Our product management and consulting teams who work with state and local government leaders to implement our CGI Advantage ERP solution have participated in a workshop series on Ethics and Equity in Data to learn how to implement ethical and equitable data concepts. These teams are already applying the concepts as they consult with our clients on their CGI Advantage ERP solution implementations.
- CGI is also partnering with LA Based Company, LATech4Good, to offer workshops on data justice—providing clients with skills and tools to begin applying ethics and equity to their data practices. The impact of those sessions resulted in a 73% increase in participants confidence in the knowledge and application of data equity.
- America’s hospitals and health systems have stepped-up data initiatives to ensure high-quality, equitable and safe care is available for all. As an example, the American Hospital Association (AHA), launched its #123forEquity pledge campaign that builds on the efforts of the National Call to Action to Eliminate Health Care Disparities to challenge hospital and health system leaders to increase the collection and use of patient race, ethnicity, language - and other personal information. The initiative also encourages systems to leverage data to drive improvement in healthcare management and, ultimately, eliminate health disparities.
How innovative technologies enable providers and patients to improve data collection
Since the launch of the campaign, progress has been made, but the glaring opportunities to leverage AI, build IT systems that mitigate for bias in data collection, and streamline integrated data sharing systems more deliberatively remain unrealized. Innovative technologies enable providers and patients to improve data collection to better understand risks, solutions, and outcome-centered approaches to managing individual and population health.
As a former healthcare diversity, inclusion and health equity officer, I’ve seen firsthand the critical role that AI can play in collecting essential patient data and improving contact tracing and disease mapping for vulnerable populations. Collecting and analyzing patient information with data justice principles in mind is a proven strategy for driving better decision-making. We have the opportunity to take the lessons learned over the last two years as well as calls to action, like the AHA’s #123forequity pledge, to address the data gaps that still persist and be better positioned to achieve greater equity in the future.
To advance these concepts and deepen the dialog on the importance of data, IP, and artificial intelligence in advancing health equity and eliminating health disparities, listen to our panel discussion "Data Justice: Ethical Practices for Equitable Health".