SHIFT 2
Removing Racism from Clinical, Operational, and Administrative Diagnostics, Algorithms, and Processes
Race-based corrections and proxies for race (like insurance status) have become embedded in healthcare clinical and operational diagnostics, algorithms, and processes in ways that perpetuate racial inequities. This shift challenges us to begin to account for and eliminate the way we use race and proxies for race from our processes. A few resources you might find helpful are found here.
We Embed Race–and Proxies for Race–in Ways that Marginalize and Harm Communities of Color.
Over the last five years, the number of articles documenting how race–and its proxies have been used to marginalize communities of color and communities in poverty–who are often the same–have skyrocketed.
What are Examples of Racism in our Diagnostics, Processes and Algorithms?
Is the way we correct for race (for example in Glomerular Filtration Rate (GFR) measurement and other examples of race-based corrections) creating disadvantage for that group?
Are social assessment protocols precluding some groups from receiving organ transplants by eliminating those who have greater social needs?
Are insurance verification algorithms used by the healthcare organization for accepting people into elective procedures favoring people with certain insurance types and dissuading people of color?
Is the marketing or strategic process to identify the community where a new building will be placed deprioritizing communities with higher poverty rates who might be at the greatest need?
Are procurement protocols disincentivizing small and minority owned businesses from competing?
What are some examples of racism not (yet) measured
Any measure of a process, policy or practice that drives racial inequities that your organization is not currently focused on measuring and improving could fit into this category. Here are a few examples:
Wait times being much longer for people of color or on those on Medicaid compared with patients on commercial insurance
Is financial assistance being proactively and respectfully offered to all groups? Are medical debt collection practices reinforcing inequities for communities of color?
Assessing whether catchment areas for community benefits resemble racially gerrymandered maps and correcting these
What Skills will be Developed within this Shift?
Understanding how racist diagnostic and screening algorithms have and continue to exclude communities of color.
Working with patients and communities to acknowledge and address harm.
Identifying inequitable diagnostic and screening procedures and understand what can be done to mitigate them.
Development of a system change strategy to build new systems.
Development of an equitable governance structure to oversee improvement that includes community members affected by inequities.
Racism in Clinical Diagnostics and Algorithms.
Video: A brief understanding of how racism in clinical diagnostics and algorithms leads to inequities
Report: How the System of Organ Transplantation Can be Reformed
Story of real change:
Database of race-based algorithms
Connect with organizations working on these issues: Council of Medical Specialty Societies:
STAT: Explore a series of articles and resources from Stat News about Embedded Bias
How Healthcare Debt Collection Processes puts Communities of Color into Greater Poverty?
Connect with organizations working on the issues: