At the June 21 convening "Together to Catalyze Change for Racial Equity in Clinical Algorithms", CMSS announced the launch of Encoding Equity in Clinical Research & Practice: Rethinking Race in Clinical Algorithms. This new alliance will be led by the Council of Medical Specialty Societies and generously supported by the Doris Duke Foundation.

The Encoding Equity alliance will drive change to ensure that equity is encoded in algorithms which reflect unbiased and valid evidence. Our goals include changing the way in which race is considered in research design and ensuring that patients— especially those who have been harmed by biased algorithms—experience the best possible health outcomes. This new effort will build on the work already underway and shared during today’s convening, coordinate efforts across sectors, share best practices for revisiting existing algorithms, educate, and disseminate information on the urgent need to reconsider the role of race in clinical algorithms.

CMSS is incredibly grateful to the Doris Duke Foundation for their support of the Encoding Equity Alliance, which will enable us all to accelerate change more urgently and comprehensively than any one organization or group of stakeholders can. We look forward to sharing more details on this new alliance to follow. Visit EncodingEquity.org to learn more and join us in taking action to advance health equity.

“As a convener of medical societies, CMSS is uniquely positioned to bring the clinical and research communities together to foster collective action and amplify impact. The additional focus brought by the Encoding Equity alliance structure will enable us to accelerate change more urgently and comprehensively than any one organization or group of stakeholders can. We seek to engage and activate more individuals and organizations across the medicine, research, funding, publishing and technology sectors to share in designing better tools for medicine.” -Helen Burstin, MD, MPH, CMSS CEO

Learn more about CMSS's prior efforts in rethinking race in clinical algorithms.