Together To Catalyze Change by CMSS Admin

TOGETHER TO CATALYZE CHANGE

Algorithms

A meeting sponsored by the Doris Duke Foundation, the Council of Medical Specialty Societies, and the National Academy of Medicine

October 23, 2024

EXECUTIVE SUMMARY

This report highlights key moments from the conversations that occurred during Together to Catalyze Change, which described the importance of revisiting past assumptions, highlighted recent progress, delved into issues with artificial intelligence (AI)-driven algorithms, and reviewed outstanding questions and challenges.

The meeting offered a window into the remarkable progress that has been made when communities come together to address these challenges head-on. From the powerful opening keynote that highlighted the many ways in which past assumptions about the role of race in clinical algorithms have not held true, to the ways in which those assumptions have caused harm, and the path to righting these past wrongs, meeting attendees dug deep into the opportunities and obstacles on the road to change.

Case Studies: Organizations in Action

Highlighting meaningful progress, Together to Catalyze Change featured case studies that discussed the efforts of professional societies and others to address problematic algorithms and clinical guidelines. These case studies described:

• Broadening a society’s focus beyond algorithms to include clinical practice guidelines and policy statements, from the American Academy of Pediatrics

• The development of a new risk calculator that uses race as one dimension on which to evaluate the algorithm’s performance, instead of using race as the only factor, from the American Heart Association

• The importance of focusing on both the risks of major clinical events and the outcomes that patients experience after such events, from the American Society of Bone & Mineral Research

Artificial Intelligence & Clinical Algorithms and Guidelines

While AI-derived clinical algorithms and practice guidelines are not yet in extensive use, interest in them and applications for their use is growing, so the convening also explored the opportunities that abound for close partnerships between the AI community, clinicians, and researchers to help address a wide range of challenges, such as the lack of agreed-upon measures of bias and fairness for AI models.

EXECUTIVE SUMMARY

Compelling Questions and Practical Solutions

Despite the significant progress that has been made in understanding the impact of race in clinical algorithms and guidelines, and in de-implementing some of those that have caused harm, there are a wide range of outstanding issues and questions that meeting attendees grappled with in small groups, such as:

• How many algorithms and guidelines exist that use race as a proxy for genetics, biology, and bias?

• How can we foster more rigorous research that informs algorithm and guideline development?

• How can funding and publication policies be changed to advance equity?

• How can we broaden guideline panel membership to advance equity?

• How should researchers address the most pressing implementation challenges?

• How can we collaboratively engage a broad range of partners in these efforts?

• How can we communicate effectively on these issues?

New Alliance Launch

The highlight of the meeting was the launch of Encoding Equity in Clinical Research & Practice: Rethinking Race in Clinical Algorithms. This new Alliance, led by the Council of Medical Specialty Societies with support from the Doris Duke Foundation, is designed to:

• Ensure that equity is encoded in algorithms and guidelines so that they reflect unbiased and valid evidence;

• Highlight the impact of outdated algorithms and guidelines on clinical practice, hospital operations, and on patient care and outcomes;

• Change the way in which race is considered in research design and ensure that patients—especially those who have been harmed by biased algorithms and guidelines— experience the best possible health outcomes;

• Convene and activate leaders in clinical medicine and research, technology/AI, publishing, and philanthropy in the design, implementation, and monitoring of clinical algorithms and guidelines to alleviate harm and enhance health equity.

EXECUTIVE SUMMARY

Encoding Equity will coordinate efforts across sectors, share best practices for revisiting existing algorithms, and disseminate information on the urgent need to reconsider the role of race in clinical algorithms. This new Alliance will build the movement across communities of influence that can make a real difference in the practice of medicine and in the lives of patients.

Read the full report for more detail on the elements described above. The closing section of this report also highlights the Encoding Equity Alliance, its planned activities, and ways to engage.

Together, we can address equity and improve outcomes for patients who have been harmed by the use of historical and misguided assumptions about race.

INTRODUCTION

Medicine has reached a tipping point.

We have been studying racial and ethnic inequities in healthcare for decades.

We have seen how inequities affecting Black, Indigenous, and people of color result in differences in how care is delivered and result in harm to patients.

We have talked about the need for change and been frustrated by the lack of change.

Now we have reached a point at which, as Dr. Helen Burstin, Council of Medical Specialty Societies, boldly declared, “Inaction is unacceptable.”

Algorithms and guidelines are omnipresent in healthcare, including in areas as diverse as how kidney function is measured, which young children are tested for urinary tract infections, predicting the risk of cardiovascular disease, and determining which women receive osteoporosis screening and treatment.

Many of these algorithms and guidelines create an insidious problem: by treating race as a biological construct rather than a social concept, they create and perpetuate inequities in healthcare that cause potential harm to patients of color.

In June 2024, the convening Together to Catalyze Change for Racial Equity in Clinical Algorithms, sponsored by the Doris Duke Foundation, the Council of Medical Specialty Societies, and the National Academy of Medicine, rallied participants to take action to address one clear challenge in this area: remove race used as a biological construct from clinical research, clinical algorithms and clinical practice guidelines.

INTRODUCTION

Building on a landmark gathering that occurred in 2023,1 Together to Catalyze Change demonstrated that in just one year, more organizations are doing the work necessary to reach the desired goal. Medical societies, health systems, and researchers are moving from listening to engaging. From describing the problem to taking action. From working in siloes to sharing their findings with peers across disciplines.

To reinforce this momentum, the meeting also included the launch of a new Alliance, Encoding Equity in Clinical Research & Practice: Rethinking Race in Clinical Algorithms. Led by the Council of Medical Specialty Societies with support from the Doris Duke Foundation, Encoding Equity is designed to drive change on many fronts, ensuring that clinical algorithms and guidelines reflect unbiased and valid evidence and drive high-quality, equitable care.

Read on to learn more about both the research and practice that are energizing the field, the challenges that remain, and the wide range of partners who have a critical role to play in advancing the health equity goals that are within reach.

KEYNOTE ADDRESS

AI: Partnerships and avoiding pitfalls

University of California San Francisco Revisiting

The Impact of Assumptions

Aaron Baugh, MD

In his powerful keynote address to the Together to Catalyze Change participants, Dr. Aaron Baugh, University of California, San Francisco, highlighted the many ways in which past assumptions about the role of race in the assessment of pulmonary function have not held true, and the ways in which those assumptions have caused harm.

Dr. Baugh opened with the story of Dr. W. Montague Cobb, a researcher and activist who later went on to chair the Department of Anatomy at Howard University and then to serve as the president of the NAACP. Cobb was the only black physical anthropologist in the U.S. in the 1930s when he began studying why Black and Asian athletes were performing at higher levels than white athletes. In keeping with the approach to science at the time, Dr. Cobb searched for physical differences by race that could explain divergence in athletic accomplishment – and found none.3

Despite these findings, 30 years later Dr. Albert Damon of Harvard University undertook a body of research investigating “possible racial differences in pulmonary function.”4 Research by Dr. Damon and others informed decades of clinical care in which lung function was assumed to be innately different in Black and white patients.

Dr. Baugh described how the research conducted by Dr. Cobb and Dr. Damon exemplify the broader struggle to understand race-based medicine and the ways in which racial inequities in healthcare are created by the assumptions that drive research and clinical care. Research has repeatedly shown that assumptions of biological differences between racial groups are unfounded, and yet such research continues, along with racialized clinical care.

KEYNOTE ADDRESS

The Impact of Assumptions | Aaron D. Baugh, MD

In the context of pulmonary function, Dr. Baugh described the prevalent thinking about the role of race in clinical care:

Our assertion has been for decades that including race … was supposed to enhance our understanding. It was supposed to reduce confounding or confusion, and therefore improve our ability to understand who is sick, and who has disease.

Using spirometry – a well-established measure of lung function – Dr. Baugh illustrated how our understanding of the relationship between race and pulmonary function algorithms has evolved over time to contradict this assertion. He highlighted research that has disproven three prior assumptions: that there are innate racial differences in lung function, that social and environmental influences have little impact on lung function, and that including race in algorithms is the best approach to clinical care.

His talk called for a heightened understanding of the impact that misinterpretations about race have on clinical care, using lung function testing as an example. Skillfully debunking the three assumptions above, he called for a fundamental change in how to assess pulmonary function and how to establish the algorithms and best practices that guide clinical care.

Prior Belief

“We at one point believed that average differences in lung function across racial/ethnic groups were entirely innate.”

“We asserted that the role of social and environmental influence was pretty minimal on lung growth.”

“It was our belief that these differences by race and ethnicity were not clinically important and shouldn’t inform care, and so we needed to develop an interpretive system that [included race in algorithms and then] taught us to ignore [these differences].”

What We Have Learned

“But on examining more closely, we found that a lot of those arguments were lacking in rigor and evidence.”

“And yet, we’ve seen some significant data that contradicts that.”

“And yet, we’re amassing more and more evidence ... that when we actually examine clinical outcomes, [the inclusion of race is] ... at best, having no impact, and may, in fact, be doing the opposite [causing harm].”

Current Best Understanding

Genetics, physical size, and other biological characteristics do not explain racial differences in lung function.

When, where, and how people grow up affects their lung growth and function, and “privilege does not accrue equally by race.

Race-neutral spirometry results correlate better with key measures of lung health than results that include race.

KEYNOTE ADDRESS

The Impact of Assumptions | Aaron D. Baugh, MD

Dr. Baugh spoke to a rapt audience and closed by issuing a powerful call to action using his own personal story to challenge existing assumptions about race and its role in clinical care:

My name is Aaron Dorian Baugh. My first ancestor was a slave on the shores of Anne Arundel County. And in his legacy, I ask you … that we not content ourselves simply with describing the deficiencies of the system we have – nor that we give up in disgust and cynicism and walk away saying nothing can change – but that we have the courage to believe, [and] that you join with me in trying to build something better.

— Dr. Aaron Baugh, University of California,

San Francisco

PANEL

What Do We Know About the Impact of Clinical Algorithms and Guidelines on Racial and Ethnic Inequities?

Regardless of whether or not they include variables for race and ethnicity, clinical algorithms and guidelines have the potential to affect disparities in care. A recent systematic review sponsored by the Agency for Healthcare Research and Quality appraised a broad body of evidence to understand what those effects might be, as well as the impact of strategies to mitigate any inequities.5

Study leader Dr. Shazia Siddique shared the team’s published findings that clinical algorithms and guidelines can have a full range of impacts, as shown in the figure below.

These guidelines form the basis for institutional protocols, clinical pathways, and decision support tools that live in electronic health records.

— Dr. Joseph Wright, American Academy of Pediatrics

If an algorithm is not race conscious, if it’s not considering the effects of racism, you can still perpetuate or exacerbate disparities.

— Dr. Shazia Siddique, University of Pennsylvania

AI: Partnerships and avoiding pitfalls

PANEL

Organizations in Action

Together to Catalyze Change featured three organizations that have been actively addressing problematic algorithms and clinical guidelines in order to advance equity in healthcare: the American Academy of Pediatrics (AAP), the American Heart Association (AHA), and the American Society for Bone and Mineral Research (ASBMR).

These discussions highlight three key points that will be essential for making progress in this space: broadening the focus beyond algorithms, using race to assess whether algorithms perform equitably, and the need to revisit past assumptions.

Broadening the Focus Beyond Algorithms

Following on a close review of the impact of race on its pediatric urinary tract algorithm, the AAP engaged in a systematic approach to evaluating a broad range of AAP policies. An AAP rapid response team moved beyond a narrow focus on algorithms and found that many of their clinical practice guidelines included a problematic use of race. AAP is currently working to review a broad range of policy statements, clinical reports, clinical practice guidelines, and technical reports with an eye toward reaffirming, revising, or retiring them as appropriate.

Key takeaways: The use of race in algorithms is problematic, but focusing narrowly on algorithms will not fully address the underlying issue of health equity. Clinical practice guidelines and other documents that influence clinical practice also need to be examined and updated.

What are clinical algorithms and clinical practice guidelines?

As shared by presenter Dr. Shazia Siddique, University of Pennsylvania, a healthcare algorithm is “a mathematical formula or model that combines different input variables or factors to inform a calculation or estimate such as disease risk or prognosis.” These algorithms form the basis for many clinical calculators and risk tools currently in use, including many of those used in electronic health records to support decision making at the point of care.

In 2011, the Institute of Medicine (now the National Academy of Science) defined clinical practice guidelines as “statements that include recommendations intended to optimize patient care that are informed by a systematic review of evidence and an assessment of the benefits and harms of alternative care options.”

Both algorithms and guidelines were discussed during the convening.

PANEL

Organizations in Action

Using Race to Evaluate Algorithm Performance

AHA developed a new cardiovascular disease risk calculator that updates prior risk prediction and drew on multiple large datasets for its development. Instead of using race as a predictive variable, the new algorithm, Predicting Risk of Cardiovascular Disease Events (PREVENT), considers race as a dimension on which to evaluate the algorithm’s performance. By ensuring the model was developed from a diverse and inclusive sample, PREVENT predicts cardiovascular risk equally well across diverse racial and ethnic groups even without race as a predictor.

Key takeaways: While using race as a predictive variable in clinical algorithms can be problematic, including race in the evaluation of algorithm performance can enable an assessment of whether these algorithms are performing equitably. Large data sets with appropriate variables representing race and ethnicity as well as upstream social determinants of health can be essential tools for evaluating algorithm performance for different groups of people.

Racism impacts cardiovascular disease risk and health outcomes. These disparities are pervasive and persistent and may actually grow and widen if we don’t take action now. Prior risk models have significant limitations, largely led by the lack of diverse, representative, and contemporary populations, which may have contributed to bias in risk estimation. A better, more inclusive and more accurate model is just the first step.

—

Dr. Sadiya Khan, Northwestern University

Organizations in Action

Revisiting Past Assumptions

ASBMR evaluated whether the Fracture Risk Assessment Tool (FRAX) predicted fractures in diverse cohorts, in part drawing inspiration from a 2023 meeting on race in clinical algorithms that was convened by the Council of Medical Special Societies. The U.S. is one of only four of the 84 countries using FRAX to include race in its calculator. The ASBMR task force found little justification to support the current use of race and ethnicity as variables in FRAX. They further noted that while FRAX identifies fewer Black patients at risk of fracture, Black patients have far worse outcomes than other groups of patients, indicating that algorithms must be assessed based on both risks and outcomes.

We actually need to be looking at post-fracture outcomes and not be satisfied by saying that a certain group is at low risk for fracture if they are at higher risk for death or disability.

— Dr. Sherri-Ann Burnett-Bowie, Harvard University

Key takeaways: More research is needed to determine which algorithms are based on sound assumptions and which need to be revisited. Algorithms and clinical risk calculators based on risks of events may also need to examine the outcomes of those events in order to ensure equitable performance.

More detail on these case studies is available in Appendix 2. Additional examples of Organizations in Action can be found on the Encoding Equity website.

GUIDING PRINCIPLES FOR ACTION

Several federal agencies and policy organizations have been active around the issue of race in clinical algorithms. For example, the Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. In late 2023, the panel published the results of its deliberations on approaches for ensuring that clinical algorithms promote racial equity, including five guiding principles:6

• Promote health and health care equity during all phases of the health care algorithm life cycle;

• Ensure that health care algorithms and their use are transparent and explainable;

• Authentically engage patients and communities during all phases of the health care algorithm life cycle, and earn trustworthiness;

• Explicitly identify health care algorithmic fairness issues and trade-offs; and

• Establish accountability for equity and fairness in outcomes from health care algorithms.

Anticipated in late 2024, the National Academies of Sciences, Engineering, and Medicine will release a report from its Committee on The Use of Race and Ethnicity in Biomedical Research. This work will assess the current use of the social constructs of race and ethnicity in biomedical research and develop recommendations for their future use. The report is intended to provide recommendations to guide the scientific community in the future.

PANEL

The Evolving Role of AI and Opportunities for Partnership

While most clinical guidelines and algorithms are still developed through systematic reviews and statistical modeling, there is growing interest in using AI-derived algorithms and clinical practice guidelines. Unlike the statistical models that have been in use for many years, efforts to evaluate and mitigate bias in AI algorithms are in their early days.

The AI community is developing approaches to avoid prior pitfalls regarding racial and ethnic inequities in this realm, and is working to address:

• The lack of widely agreed-upon measures of bias and fairness for AI models;

• The challenges in optimizing AI models, as an algorithm that maximizes accurate prediction may not simultaneously maximize performance on equity and bias;

• The lack of existing criteria to determine whether or not race should be included in an AI model; and

• The lack of transparency in how AI models are being developed.

Algorithms are programs trained on our histories, so if you’re from an underrepresented part of society, that doesn’t sound too good. And yet, there’s an opportunity. If we make intentional effort to train our models so that we are more in alignment with guidelines on what fairness is, then we have the ability to address that bias.

— Dr. Brian Anderson, Coalition for Health AI (CHAI)

AI: Partnerships and avoiding pitfalls Revisiting past assumptions Progress

that remain Driving rapid transformation

PANEL

The Evolving Role of AI and Opportunities for Partnership

Opportunities abound for close partnerships between the AI community, clinicians, and researchers to help address these challenges. Such partnerships could contribute to the areas that Dr. Tina Hernandez-Boussard, Stanford University, highlighted during the convening:

• Increasing racial diversity in clinical trials to increase the racial diversity of the data on which AI models are trained;

• Promoting education and awareness about health equity; and

• The need for shared accountability between model developers and implementers.7

Partnerships are also essential to the work of the Coalition for Health AI (CHAI), which is working to develop consensus-based measures of bias and to build a network of quality assurance labs to assess AI models in healthcare.

AI May Also Have the Potential to Improve Outcomes

While there are many reasons to attend to the potential biases introduced by the use of AI algorithms, AI-derived equations also have the potential to support improved health outcomes in historically underserved populations. Dr. Kameron Matthews, Cityblock Health, described how her organization uses AI-based algorithms to better identify which of their Medicaid and dually eligible patients need additional services.

Tina Hernandez-Boussard, MD Stanford University

Kameron Matthews, MD, JD, FAAFP Cityblock Health

PANEL

The Evolving AI Policy Landscape

The U.S. Food and Drug Administration (FDA) releases Good Machine Learning Practice for Medical Device Development: Guiding Principles as part of its work on regulating Software as a Medical Device (SaMD).8 Later that year, researchers release a call for action encouraging the FDA to regulate SaMD products to “better promote fair AI-driven clinical decision tools and for preventing harm to the patients we serve.”

Guiding principles developed by a panel sponsored by the Agency for Healthcare Research and Quality (AHRQ) and the National Institute on Minority Health and Health Disparities (NIMHD) are released.10 Designed to prevent and mitigate bias in clinical algorithms, the principles include promoting equity throughout the algorithm life cycle and establishing accountability for equity in outcomes created by the algorithms.

The U.S. Department of Health and Human Services (HHS) Office of Civil Rights issues a final rule under Section 1557 of the Affordable Care Act of 2010.12 The rule clarifies that all healthcare providers participating in federal programs, such as Medicare and Medicaid, are subject to provisions that include nondiscrimination on the basis of race, color, national origin, and other characteristics in the use of patient care decision support tools, including clinical algorithms and other tools using AI and machine learning (ML).

Colorado becomes the first U.S. state to regulate AI. Under SB24-205, developers of “high risk” AI systems are subject to a comprehensive compliance framework that includes mechanisms to protect the public from “any known or reasonably foreseeable risks of algorithmic discrimination.”13 OCTOBER 2021

The White House Office of Science and Technology Policy publishes The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People 9 This white paper provides five principles for an AI Bill of Rights, including that AI algorithms should not discriminate based on race, ethnicity, and other characteristics and that they should be designed in an equitable way.

The U.S. National Institutes of Health Office of Data Science Strategy holds a workshop entitled Toward an Ethical Framework for AI in Biomedical and Behavioral Research: Transparency for Data and Model Reuse Workshop 11 This workshop discusses the landscape of ethical AI development and use, including addressing bias throughout the AI life cycle.

BREAKOUT GROUPS

Outstanding Questions: What Do We Need to Move Forward?

Despite the significant progress that has been made, there are many outstanding questions that need to be addressed to ensure that algorithms and guidelines promote equitable treatment. Meeting attendees dove into these knotty problems and explored solutions described below.

• Technical questions:

- How many algorithms and guidelines exist, and where can we find them?

- How can we improve the research that informs algorithm and clinical guideline development?

- How should we update funding and publication policies?

• Engagement and Communications questions:

- What are the best ways to broaden the community of people serving on guideline panels?

- What are the most pressing implementation challenges?

- How can we collaboratively engage a broad range of partners, such as medical specialty societies, electronic health record vendors, and healthcare system leadership?

- What are the best ways to build effective communications strategies?

AI: Partnerships and avoiding pitfalls Revisiting

BREAKOUT GROUPS

Outstanding Questions: What Do We Need to Move Forward?

TECHNICAL QUESTIONS

How Many Algorithms and Guidelines Exist, and Where Can We Find Them?

One key challenge to making progress is identifying all of the algorithms and guidelines in which the use of race needs to be addressed, which is hampered by the lack of a central registry or database of all algorithms and clinical practice guidelines in use in the United States.

Presenter Dr. Shyam Visweswaran, University of Pittsburgh, described efforts to develop a catalog of relevant clinical algorithms, including risk calculators, laboratory tests, and medication therapy recommendations that are based on race.14 His team identified 46 such algorithms via literature searches using terms related to medical or clinical calculators and race.

However, literature searches can only identify those algorithms published in indexed, peer-reviewed journals, and identifying all potentially problematic algorithms may be far more labor-intensive. Additional context on the magnitude of the task comes from the American Academy of Pediatrics, which conducted a detailed review of 149 of its approximately 400 policies and clinical guidelines, identifying 75 in need of revision (see Appendix 2 for a case study of their work).

Pittsburgh

Shyam Visweswaran, MD, PhD University of

BREAKOUT GROUPS

What Do We Need to Move Forward? | TECHNICAL QUESTIONS

How Can We Improve the Research that Informs Algorithm and Guideline Development?

There is a demonstrated need for research to reevaluate existing algorithms and guidelines through an equity lens, as well as to use race-conscious approaches to developing revised versions. This includes:

• Developing and testing a broad range of alternative approaches to data collection and analysis;

• Increased attention to diversity in research studies; and

• Educating researchers about race-conscious approaches to algorithm and guideline development to ensure that clinical practices are not harmful to patients who are Black, Indigenous, and People of Color.

We need to re-examine, as researchers, what we mean by a ‘diverse patient population’ and really justify or examine why we use these categories without quite knowing whether that is actually creating some sort of bias. The challenge for researchers is to start really questioning why we’re including covariates in an analysis, whether or not the category of the covariate is appropriate, why we’re going to be grouping certain individuals into one category over another, and perhaps challenging ourselves to dig deeper into understanding what that variable really means.

— Dr. Janine Austin Clayton,

National Institutes of Health

How Should We Update Funding and Publication Policies?

The medical field needs updated policies to steer the peer review processes for both grant applications and peer-reviewed journal content, as both affect guideline development and how such guidelines are shared with researchers and clinicians. This includes publishing Evidence to Decision (EtD) frameworks that help inform guideline panel members’ judgements about a given topic and ensure that the most important criteria are considered in the panel’s decision making.15 There is also a need to publish a separate, accompanying assessment of the research gaps that affected the guideline development process, including gaps related to inequities in care.

As summarized by presenter Dr. Shazia Siddique, “We’re sitting here revising all these algorithms, all these guidelines, but at the same exact time, new guidelines are being generated with the exact same potential of racial and ethnic bias to be introduced because we are not fixing our guideline methodology.”

BREAKOUT GROUPS

Outstanding Questions: What Do We Need to Move Forward?

ENGAGEMENT QUESTIONS

How Can We Broaden Guideline Panel Membership to Advance Equity?

Guideline panels are typically comprised of senior researchers with notable reputations, and do not often include others such as junior researchers, patients, or community-based clinicians. Engaging a broader range of researchers and clinicians may be helpful for ensuring that guidelines contribute to equitable healthcare. As noted by participant Dr. Judy Gichoya, Emory University:

“If you think about how the guidelines are really translated to clinical care beyond academic medical centers, I think we are a little bit in a bubble, and that’s a call to think about who’s not in the room.”

Carolyn Clancy, MD Veterans Health Administration

How Might We Address the Most Pressing Implementation Challenges?

Identifying problematic algorithms and guidelines and updating them is only the first step in a complex implementation process. As described by presenter Dr. Carolyn Clancy, Veterans Health Administration:

“The field has an astonishing array of very smart, thoughtful people dedicated to figuring out what’s the right thing to do, but that’s very different than actually figuring out ‘how do we do it’.”

Dr. Clancy highlighted some of the implementation challenges related to the use of the new eGFR in the labs within the Veterans Health Administration, the nation’s largest integrated healthcare system, noting that each facility faces its own implementation challenges:

“In every one of our facilities, lab medicine is an independent entity. So they all had to be ‘bought in’ on how the eGFR rate is calculated for kidney function. And of course, that is all embedded in the fact that, in theory, we have one electronic health record when in real life, we have 130 different instances of the same electronic record.”

Solving these implementation challenges will require engaging people with a broad array of expertise and responsibilities within local and national healthcare systems.

BREAKOUT GROUPS

What Do We Need to Move Forward? | ENGAGEMENT QUESTIONS

How Can We Collaboratively Engage a Broad Range of Partners?

A strong theme throughout the convening was the need for a broad range of partnerships and strong leadership to make change happen.

Presenter Dr. Sherri-Ann Burnett-Bowie, noted her appreciation for the work of the Council of Medical Specialty Societies “applying external pressure, because having this coalition of societies really encourages, if not forces, all of us to do better.”

Emphasizing the need for strong leadership and his hope for partnership across multiple specialty societies, presenter Dr. Joseph Wright, American Academy of Pediatrics, commented:

“Equitable transformation within professional societies requires full leadership investment. I have spent the better part of the last couple of months socializing this work with all components of our organization, including our board, to make sure that we have the buy in and support to move forward, because this is going to be disruptive … we are really turning the way that we develop policy upside down.”

To move forward, a broad range of partners must collaboratively engage in change, including:

• Healthcare system leaders who set institutional policy and drive implementation of new algorithms and clinical practice guidelines;

• Electronic health record and clinical decision support tool vendors, to ensure that new algorithms are incorporated into the tools that clinicians use daily;

• Laboratories, including reference laboratories, to ensure that new algorithms are incorporated into laboratory tests and the interpretation of their results;

• Funders, to discuss where research gaps to support equitable guideline development exist; and

• Specialty societies and clinical researchers, who can highlight the consequences that result from the use of race in clinical algorithms for the research community.

Sometimes you have to think about different collaborators, people that you don’t know, people that you didn’t train with, but there are people out there who can help you out with diverse patient populations and with research, but you have to think outside the box.

— Dr. Marilyn Foreman, Morehouse School of Medicine

BREAKOUT GROUPS

What Do We Need to Move Forward? | ENGAGEMENT QUESTIONS

How Can We Communicate Effectively?

Meeting participants connected to discuss challenges in communicating effectively with various audiences, including healthcare professionals, health systems, and members of the public, on complex topics. Through the discussion, attendees shared challenges they have encountered as well as effective communication strategies they have employed. Some themes included:

• Engaging patiently in deep conversations to fully understand and address questions, concerns, misconceptions, and motivations.

• Sharing the history of how race came to be included in algorithms, the limited evidence for race as a biological construct, and the historical ways in which race has been used in algorithms and has caused harm.

• Leveraging the power of storytelling, particularly highlighting patient voices, which makes the science personal and speaks to a lived experience that is relatable.

• Ensuring that the topic is understood as an issue of health care quality and safety which improves outcomes for patients.

As stated by meeting participant Dr. Justin List, Veterans Health Administration, “The work for me has often been having patient, slow, iterative conversations to change hearts and minds.”

The new Encoding Equity Alliance will provide an essential community for ongoing discussion within and across stakeholder groups seeking to advance health equity. The Alliance will also curate and amplify resources and news stories for the community. A series from STAT News is included in the sidebar as an example of communications strategies individuals and organizations can employ to inform and engage audiences with our work.

We start with terminology and ground everything in history. We have to show where these ideas and practices came from and why they were and still are being used. Because then, by the time we get to ‘So this is where we’re at and this is what needs to change’ … there should be very little, to no, resistance.

In September 2024, STAT published a series of articles, Embedded Bias: the struggle over removing race from clinical algorithms

— Dr. Ijeoma Nnodim Opara, Wayne State University

LAUNCH

Driving Rapid Transformation: Encoding Equity

Together to Catalyze Change marked the launch of Encoding Equity in Clinical Research & Practice: Rethinking Race in Clinical Algorithms, led by the Council of Medical Specialty Societies with funding from the Doris Duke Foundation.

Encoding Equity is an Alliance designed to:

• Ensure that equity is encoded in algorithms and guidelines that reflect unbiased and valid evidence;

• Highlight the impact of outdated algorithms and guidelines on clinical practice, hospital operations, and on patient care and outcomes;

The Alliance will be supported by an Advisory Committee to guide its efforts and Task Forces to develop specific action plans.

MISSION

We believe inaction is unacceptable.

The Encoding Equity Alliance is committed to driving change in clinical research and practice, identifying inappropriate use of race in algorithms and guidelines, redesigning more accurate and equitable decision tools, and collecting and communicating evidence to advance health equity.

Encoding Equity galvanizes collective action to amplify our impact, making change more urgently and comprehensively than any one organization or group of stakeholders can.

The Alliance engages and activates more individuals and organizations across the medicine, research, funding, publishing, and technology sectors to take leadership, ownership, and action to advance health equity.

VISION

We envision a future where the appropriate use of race in research design and clinical guidance will drive toward equity—a future where the tools that guide patient care are informed by rigorous methods and strong evidence to prevent misuse of race as a biologic construct.

This is a future where more equitable clinical decision-making tools lead to more equitable health outcomes.

Driving Rapid Transformation: Encoding Equity

The Alliance will build on the work of its organizations in action, beginning with the American Academy of Pediatrics, the American Heart Association, the American Society of Hematology, the American Society of Nephrology, the American Thoracic Society, and the Coalition to End Racism in Clinical Algorithms (CERCA).

Adding to this initial group of organizations, Encoding Equity seeks to engage funders, publishers, medical societies, researchers, the AI/ML community, patient groups, and individuals at all current levels of effort: The Alliance welcomes those interested and considering engaging, those currently exploring the issue, those in the planning stages or early engagement, those currently reconsidering race in algorithms and guidelines, and those focused on the implementation of updated algorithms and guidelines.

Passion for understanding why and how we got here is key to understanding how we move forward. I look across this room, everyone here has the same passion. The challenge for us is never forgetting that passion as we move forward and face challenges from contrarians and naysayers who do not wish to move forward into a new world where medicine is delivered with equity, brought together with the cutting-edge research which is based on our diverse population.

— Dr. Cynthia Delgado, San Francisco Veterans Affairs Health Care System and University of California, San Francisco

Driving Rapid Transformation: Encoding Equity

The Alliance will work in four areas:

• Discover & Change: Identify racial inequities in algorithms and guidelines, revise these decision tools, and put them into clinical practice;

• Drive Knowledge: Educate, inform, and advocate for a more rigorous approach to how race is applied and understood in the design of algorithms, guidelines, and the care decisions they inform;

• Develop & Share Best Practices: Share strategies and best practices to prevent inequities in clinical research design and decision tools going forward; and

• Raise Awareness: Amplify evidence-based research and educate stakeholder communities to advance health equity.

Encoding Equity activities will include:

• Ongoing convenings to share lessons learned, build collaborative relationships, and advance the field;

• Competitive grants to specialty societies and their research partners to support efforts to assess and address the use of race in clinical algorithms and guidelines and support further engagement with researchers;

• Educational outreach activities to promote understanding and spread best practices; and

• A change management toolkit to highlight best practices that support assessing, updating, and de-implementing problematic clinical algorithms and clinical guidelines, and to support building collaborative strategies with the research community.

This new Alliance will build the movement across communities of influence that can make a real difference in the practice of medicine and the lives of the patients we are privileged to serve.

Join us, take action, and learn more at Encoding Equity.

APPENDIX 1: RESOURCES

Together to Catalyze Change for Racial Equity in Clinical Algorithms was held on June 21, 2024 in Washington, D.C.

Additional information is available at these links:

• Meeting home page

• Agenda

• Speakers

• Attendees

APPENDIX 2: CASE STUDIES

Establishing a Race-Conscious Approach to Clinical Guidance in Pediatric Care

In 2022, the American Academy of Pediatrics (AAP) published a policy statement indicating that the Academy would “critically examine all policies and practice guidelines for the presence of race-based approaches” and would revise or retire “all policies and practice guidelines that include race assignment as part of clinical decision-making.”16

In keeping with this policy, in 2023 the AAP undertook a critical examination of 149 of its approximately 400 policies and clinical practice guidelines. To begin, the AAP convened a rapid response team charged with establishing a methodology to identify and assess race-based AAP policies.

As shared by presenter Dr. Joseph Wright, this team established an evaluation rubric with five criteria for revising practice guidelines:

• Explicit mention of race and/or ethnicity in problematic ways, such as using race as a biologic proxy for another measure or as an independent dichotomizing clinical risk-determining variable;

• Reliance on flawed clinical evidence in its consideration of race/ethnicity;

• Absence of reference to existing inequities or outcome disparities already known in the scientific literature;

• Mention of outcome disparities without discussion of drivers, etiology, or proposed solutions; and

• Figures, tables, and/or images that are either not inclusive or present race/ethnicity in a problematic manner.

Using these criteria and a series of reviews, the Rapid Revision Team identified 75 clinical reports or clinical practice guidelines in need of review. Prioritizing those reports based on three domains (equity assessment, impact on clinical care, and alignment with organizational priorities), they ultimately selected three guidelines on which to engage in more extensive revision efforts as a pilot for broader work.

In the longer run, this approach to the review of race and ethnicity in clinical reports and clinical practice guidelines will be embedded into the AAP’s regular five-year review cycle in which each policy statement, clinical report, clinical practice guideline, and technical report is reviewed and either reaffirmed, revised, or retired.

CASE STUDIES

Evolution of Cardiovascular Disease Risk Models

Cardiovascular disease is one of the leading causes of mortality in the U.S., with estimates by that by 2050, 45 million U.S. adults will have cardiovascular disease.17 In addition, racial disparities in cardiovascular disease are well documented, pervasive, and persistent. Black adults have significantly higher rates of cardiovascular disease than white adults, and these disparities are anticipated to grow over the next 30 years.

Identifying patients at increased risk for cardiovascular disease is therefore essential to improving population health, and the AHA’s approach was described by presenter Dr. Sadiya Khan.

Yet historically, cardiovascular disease risk models either were based on largely white populations (the Framingham Heart Model released 1998) or on a broader population that included race without closer examination of the impact of its inclusion on patients (the Pooled Cohort Equations released in 2013).

In 2023, the American Heart Association released the Predicting Risk of Cardiovascular Disease Events (PREVENT) risk calculator, based on newer data from a large, racially diverse pooled data set. This model does not include race as a variable and performs significantly better than its predecessors overall. In addition, unlike the Pooled Cohort Equations, PREVENT performs equally well across racial and ethnic groups. Funding from the American Heart Association’s De-biasing Clinical Care Algorithms project will support further research to assess PREVENT’s performance by education, income, housing stability, social support, and transportation access.

CASE STUDIES

Addressing Racial Disparities in Fracture Risk Assessment

Osteoporosis and hip fractures are increasing globally and in the U.S., with the number of people at high risk of fracture expected to double over the 30-year period from 2010 to 2040. This is particularly concerning because within one year after hip fracture, patients have a 24% chance of dying and a 33% chance of no longer being able to live independently.18,19

The Fracture Risk Assessment Tool (FRAX) to assess the risk of major bone fractures was developed by the University of Sheffield in 2008 using data from a mostly white population. Of the 84 countries in which FRAX is currently in use, the U.S. is one of only four that includes race in its FRAX calculations, based in part on observations that Black patients have a lower risk of fractures than white patients. However, the race and ethnicity adjustment factors in the FRAX calculator have not been validated. As a result, the U.S. FRAX level of accuracy may be systematically worse for individuals who are Black than for those who are white.

Further, FRAX adjusts for race twice – once by applying an explicit race adjustment factor and a second time by failing to account for the higher average bone density of Black patients. As a result, the tool identifies fewer Black patients at risk of fracture, resulting in lower treatment rates at the same time that Black patients are more likely to die, to enter a long-term nursing facility, or to become newly eligible for Medicaid in the year after a fracture.20

In 2021, the American Society for Bone and Mineral Research convened a task force to examine the U.S. FRAX.21 The task force found little justification to support the current use of race and ethnicity as a variable in the calculator, noting the small numbers of minority patients with fractures in the studies used to asses FRAX performance and the lack of consideration of racial and ethnic differences in the consequences of fracture.

The task force also recommended that non-race and non-ethnicity based fracture prediction tools should be developed using population-based fracture data that reflect changing U.S. demographics. In addition, they recommended the collection of data on individual social determinants of health (such as income and living arrangements), structural social determinants of health (such as air quality and food deserts), and the collection of data on post-fracture outcomes in racially and ethnically diverse cohorts of patients.

APPENDIX 3

REFERENCES

1 Council of Medical Specialty Societies. 2023. Reconsidering Race in Clinical Algorithms: Driving Equity through New Models in Research and Implementation. https://cmss.org/ reconsidering-race-in-clinical-algorithms-driving-equity-through-new-models-inresearch-and-implementation/

2 Institute of Medicine. 2011. Clinical Practice Guidelines We Can Trust. Washington, DC: The National Academies Press. https://doi.org/10.17226/13058.

3 Cobb WM. 1936. Race and Runners. The Journal of Health and Physical Education 7(1): 3-56. DOI: 10.1080/23267240.1936.10627128

4 Damon A. 1966. Negro-White Differences in Pulmonary Function (Vital Capacity, Timed Vital Capacity, and Expiratory Flow Rate). Human Biology 38 (4): 380-393. https://www. jstor.org/stable/41448805

5 Tipton K, Leas BF, Flores E, Jepson C, Aysola J, Cohen J, Harhay M, Schmidt H, Weissman G, Treadwell J, Mull NK, Siddique SM. 2023. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Comparative Effectiveness Review No. 268. (Prepared by the ECRI-Penn Medicine Evidence-based Practice Center under Contract No. 75Q80120D00002.) AHRQ Publication No. 24-EHC004. Rockville, MD: Agency for Healthcare Research and Quality; December 2023. Addendum April 2024. DOI: https://doi.org/10.23970/AHRQEPCCER268.

6 Chin MH, Afsar-Manesh N, Bierman AS, Chang C, Colón-Rodríguez CJ, Dullabh P, Duran DG, Fair M, Hernandez-Boussard T, Hightower M, Jain A, Jordan WB, Konya S, Moore RH, Moore TT, Rodriguez R, Shaheen G, Snyder LP, Srinivasan M, Umscheid CA, Ohno-Machado L. 2023. Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care. JAMA Netw Open. Dec 1; 6(12):e2345050. doi: 10.1001/jamanetworkopen.2023.45050. PMID: 38100101; PMCID: PMC11181958.

7 Hernandez-Boussard T, Siddique SM, Bierman AS, Hightower M, Burstin H. 2023. Promoting Equity In Clinical Decision Making: Dismantling Race-Based Medicine. Health Aff (Millwood) 42(10):1369-1373. doi: 10.1377/hlthaff.2023.00545. PMID: 37782875; PMCID: PMC10849087.

8 U.S. Food & Drug Administration. 2021. Good Machine Learning Practice for Medical Device Development: Guiding Principles. https://www.fda.gov/medical-devices/ software-medical-device-samd/good-machine-learning-practice-medical-devicedevelopment-guiding-principles

9 White House Office of Science and Technology Policy. 2022. Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People. https://www. whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf

REFERENCES

10 Chin MH, Afsar-Manesh N, Bierman AS, Chang C, Colón-Rodríguez CJ, Dullabh P, Duran DG, Fair M, Hernandez-Boussard T, Hightower M, Jain A, Jordan WB, Konya S, Moore RH, Moore TT, Rodriguez R, Shaheen G, Snyder LP, Srinivasan M, Umscheid CA, Ohno-Machado L. 2023. Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care. JAMA Netw Open. Dec 1;6(12):e2345050. doi: 10.1001/jamanetworkopen.2023.45050. PMID: 38100101; PMCID: PMC11181958.

11 National Institutes of Health Office of Data Science Strategy. 2024. Toward an Ethical Framework for AI in Biomedical and Behavioral Research: Transparency for Data and Model Reuse Workshop. https://datascience.nih.gov/artificial-intelligence/initiatives/ ethicalframework2024.

12 Department of Health and Human Services Centers for Medicare & Medicaid Services. 2024. Nondiscrimination in Health Programs and Activities. Federal Register 89(88): 37522-37703. https://www.federalregister.gov/documents/2024/05/06/2024-08711/ nondiscrimination-in-health-programs-and-activities.

13 Colorado General Assembly. 2024. Consumer Protections for Artificial Intelligence. SB 24-205. https://leg.colorado.gov/bills/sb24-205

14 Visweswaran S, Sadhu EM, Morris MM, Samayamuthu MJ. 2023. Clinical Algorithms with Race: An Online Database. medRxiv [Preprint]. Jul 6:2023.07.04.23292231. doi: 10.1101/2023.07.04.23292231. PMID: 37461462; PMCID: PMC10350134.

15 Moberg J, Oxman AD, Rosenbaum S, Schünemann HJ, Guyatt G, Flottorp S, Glenton C, Lewin S, Morelli A, Rada G, Alonso-Coello P; GRADE Working Group. 2018. The GRADE Evidence to Decision (EtD) framework for health system and public health decisions. Health Res Policy Syst. 16(1):45. doi: 10.1186/s12961-018-0320-2. PMID: 29843743; PMCID: PMC5975536.

16 Wright JL, Davis WS, Joseph MM, Ellison AM, Heard-Garris NJ, Johnson TL; AAP Board Committee on Equity. 2022. Eliminating Race-Based Medicine. Pediatrics 150(1):e2022057998. doi: 10.1542/peds.2022-057998. PMID: 35491483.

17 Joynt Maddox KE, Elkind MSV, Aparicio HJ, Commodore-Mensah Y, de Ferranti SD, Dowd WN, Hernandez AF, Khavjou O, Michos ED, Palaniappan L, Penko J, Poudel R, Roger VL, Kazi DS; American Heart Association. 2024. Forecasting the Burden of Cardiovascular Disease and Stroke in the United States Through 2050–Prevalence of Risk Factors and Disease: A Presidential Advisory From the American Heart Association. Circulation. doi: 10.1161/CIR.0000000000001256. Epub ahead of print. PMID: 38832505.

REFERENCES

18 International Osteoporosis Foundation. Undated. Osteoporosis & Fractures. Available at https://www.osteoporosis.foundation/educational-hub/material/infographics.

19 International Osteoporosis Foundation. Undated. Osteoporosis & Fractures: The Socioeconomic Burden. Available at https://www.osteoporosis.foundation/educational-hub/ material/infographics.

20 Ruiz-Esteves KN, Teysir J, Schatoff D, Yu EW, Burnett-Bowie SM. 2022. Disparities in osteoporosis care among postmenopausal women in the United States. Maturitas 156:25-29. doi: 10.1016/j.maturitas.2021.10.010. Epub 2021 Oct 24. PMID: 35033230.

21 Burnett-Bowie SM, Wright NC, Yu EW, Langsetmo L, Yearwood GMH, Crandall CJ, Leslie WD, Cauley JA. 2024. The American Society for Bone and Mineral Research Task Force on clinical algorithms for fracture risk report. J Bone Miner Res. 39(5):517-530. doi: 10.1093/jbmr/zjae048. PMID: 38590141.

This report was prepared for the Council of Medical Specialty Societies by Robin M. Weinick, PhD, Principal, Resonant, LLC with input and feedback from:

• Helen Burstin, MD, MPH, MACP, Chief Executive Officer, Council of Medical Specialty Societies

• Sindy Escobar Alvarez, PhD, Program Director for Medical Research, Doris Duke Foundation

• Ndifreke Ikpe, MHA, Civic Science Fellow, Council of Medical Specialty Societies

• Sarah Imhoff, MPA, Senior Program Director, Council of Medical Specialty Societies

• Kimberly Lezak, PhD, Managing Director, Biomedical Research Grantmaking, Health Resources in Action

• Julia Peterson, CAE, Chief Operating Officer, Council of Medical Specialty Societies

• Suzanne Pope, MBA, Senior Advisor, Council of Medical Specialty Societies

• Luise Moskowitz, Senior Associate, SteegeThomson

• Denise Portner, Principal Emerita, Senior Consultant, SteegeThomson

Photography: Ian Wagreich, Capitol Hill Photo Design: Pon Angara, Barkada Creative

Together To Catalyze Change