DBMI Special Seminar Series: Toward Diversity, Equity, and Inclusion in Informatics, Health Care, and Society
The Columbia Department of Biomedical Informatics announced a series of talks entitled “DBMI Special Seminar Series: Toward Diversity, Equity, and Inclusion in Informatics, Health Care, and Society.”
These talks, which began during the 2021 spring semester and are open to the public, focus on informatics research topics related to diversity, equity, and inclusion and are part of the weekly DBMI Seminar, a 1-credit course for DBMI students who can benefit from hearing new methods of research from speakers from both academia and industry.
Seminars that are part of this series will be posted below, while upcoming seminars will be listed on the DBMI Seminar page.
Title: Standardizing the Unstandardizable: The Case of Sex and Gender
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Meeting ID: 981 0245 9573
Abstract: In 2015, notice number NOT-OD-15-102 was released by the National Institutes of Health. The notice specified “consideration of sex as a biological variable” (SABV), requiring submission of information regarding this new construct from 2016 onward. However, despite this imperative explicitly citing enhancement of reproducibility, it did not lay out any conceptualization of what SABV meant, in non-human animal or human contexts, and it relied heavily on binarist and gender essentialist assumptions, which have ultimately confused the situation further. This confusion has led to SABV being co-opted by transphobic and intersexphobic organizations and individuals, while not necessarily impacting reproducibility. Why are sex and gender such complicated variables to consider? How did these constructs come to exist within the purview of scientific analysis? And what work is being done to untangle the current situation? This talk will aim to discuss these questions, while also considering the deeper ideologies underlying current scientific research and sociopolitical agendas, and how they affect effective modeling of sex and gender constructs in informatics and beyond.
Bio: Clair Kronk (she/her) is a postdoctoral fellow at the transitioning Yale Center for Medical Informatics (YCMI). She is the creator and sole author of the first LGBTQIA+ controlled vocabulary for usage in health care settings, the Gender, Sex, and Sexual Orientation (GSSO) ontology, which contains information on over 15,000 terms. Dr. Kronk has provided valuable input on GSSO standards for a number of organizations, including the Health Level 7 (HL7) Gender Harmony Project (GHP), the Systematized Nomenclature of Medicine (SNOMED), Canada Health Infoway (CHI), the International Organization for Standardization (ISO), Queensland Health, the National Academies of Sciences, Engineering, and Medicine (NASEM), the United States Core Data for Interoperability (USCDI), the World Health Organization (WHO), the Trans Metadata Collective (TMDC), the Homosaurus, Wikidata, and the American Medical Informatics Association (AMIA) Diversity, Equity, and Inclusion (DEI) Task Force.
Title: Algorithmic bias and data platforms
Abstract: We’re increasingly aware of the many ways that algorithms can encode and scale up racial bias. When designed with careful attention to label choice, algorithms can also be used to counter biases present in the health care system and ingrained in medical knowledge. To do so effectively, researchers and product developers must have access to platforms on which they can access health data for the benefit of patients and society.
Bio: Ziad trained as an emergency doctor – and he still gets away as often as he can, to a hospital in rural Arizona, to work in the ER. But these days, he spends most of his time on research and teaching at Berkeley. Inspired by his clinical practice, he builds machine learning algorithms that help doctors make better decisions. He also studies where algorithms can go wrong, and how to fix them: his work on algorithmic bias has been highly influential both in public debate about algorithms, and in regulatory oversight and civil investigations. He is a Chan Zuckerberg Biohub Investigator, a Faculty Research Fellow at the National Bureau of Economic Research, and has been named an emerging leader by the National Academy of Medicine. His work has won numerous awards, and appeared in a wide range of journals (Science, Nature Medicine, the New England Journal of Medicine, leading computer science conferences). He is a co-founder of Nightingale Open Science, a non-profit that makes massive new medical imaging datasets available for research, and Dandelion, a platform for AI innovation in health. Before coming to Berkeley, he was an Assistant Professor at Harvard Medical School and a consultant at McKinsey & Co.
Title: Advancing Health Equity through the use of Data
At the presenter’s request, this session was not recorded.
Bio: Julia Iyasere, M.D., is the Executive Director of the Dalio Center for Health Justice at NewYork- Presbyterian. In this role, she leads the Center’s efforts to address longstanding health inequities due to race, socio-economic differences, limited access to care, and other complex factors that impact the wellbeing of our communities. Dr. Iyasere attended Yale University for her B.S. in Biology and Columbia University for her M.D./M.B.A. After completing her residency in Internal Medicine at Columbia, Dr. Iyasere joined the Division of General Medicine at Columbia in 2012. Prior to her current role, Dr. Iyasere was the Associate Chief Medical Officer for Service Lines and the Co-Director of the Care Team Office at NYP. An Assistant Professor of Medicine, Dr. Iyasere continues to see patients as an internist in the Section for Hospital Medicine at Columbia.
Title: Using Machine Learning to Increase Equity in Healthcare and Public Health
Abstract: Our society remains profoundly unequal. Worse, there is abundant evidence that algorithms can, improperly applied, exacerbate inequality in healthcare and other domains. This talk pursues a more optimistic counterpoint — that data science and machine learning can also be used to illuminate and reduce inequality in healthcare and public health — by presenting vignettes about women’s health, COVID-19, and pain.
Bio: Emma Pierson is an assistant professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion, and a computer science field member at Cornell University. She holds a secondary joint appointment as an Assistant Professor of Population Health Sciences at Weill Cornell Medical College. She develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by best paper, poster, and talk awards, an NSF CAREER award, a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, MIT Technology Review 35 Innovators Under 35, and Forbes 30 Under 30 in Science. Her research has been published at venues including ICML, KDD, WWW, Nature, and Nature Medicine, and she has also written for The New York Times, FiveThirtyEight, Wired, and various other publications.
Title: Achieving TechQuity
(seminar was not recorded at the request of the Dr. Clark)
Abstract: Open discussions of social justice and health inequities may be an uncommon focus within information technology science, business, and health care delivery partnerships. However, the COVID-19 pandemic—which disproportionately affected Black, indigenous, and people of color—has reinforced the need to examine and define roles that technology partners should play to lead anti-racism efforts through our work. In this hour, we will discuss the imperative to prioritize TechQuity, and addressing social contexts in the implementation of AI and other technologies.
Bio: Cheryl Clark MD, ScD, is an Assistant Professor of Medicine at Harvard Medical School and a Hospitalist, social epidemiologist and Associate Chief in the Brigham and Women’s Hospital Division of General Medicine and Primary Care for Equity Research & Strategic Partnerships. Dr. Clark’s research focuses on social determinants of cardiometabolic health in diverse and aging populations. She is principal investigator for community engagement in the New England hub of the National Institutes of Health All of Us Research Program and chaired the social determinants of health (SDOH) Task Force that developed the SDOH participant provided information survey for All of Us. Dr. Clark serves on the Mass General Brigham Predictive Analytics committee to provide equity review of algorithms considered for clinical implementation. Dr. Clark chaired the COVID-19 equity response team during the early phase of the COVID-19 pandemic in 2020. She is the inaugural recipient of the Equity, Social Justice and Advocacy Award from Harvard Medical School and Harvard School of Dental Medicine.
Title: Racial and Ethnic Differences in Genetic Testing Uptake and Results among Young Breast Cancer Survivors: Looking Ahead at Future Work
(seminar was not recorded at the request of the Dr. Jones)
Abstract: Genetic testing for hereditary breast and ovarian cancer (HBOC) syndrome (e.g., BRCA1/2 genes) is recommended for all young women diagnosed with breast cancer at ≤ age 45, yet there is an underutilization of this critical test among this population. In this presentation, I will provide an overview of the current landscape of genetic testing and discuss my program of research that focuses on racial and ethnic differences in genetic testing uptake and results among young breast cancer survivors (YBCS). In addition, I will provide an overview of my current and future work including our innovative web-based decision aid intervention, RealRisks, that we are adapting for racially/ethnically diverse young breast cancer survivors in order to increase access to genetic testing and family risk communication. A special emphasis is placed on promoting health equity and reducing cancer health disparities.
Bio: Dr. Jones is an Assistant Professor of Nursing at the Christine E. Lynn College of Nursing at Florida Atlantic University. She obtained a Bachelor’s of Science in Nursing degree from Seton Hall University and a Master’s of Science in Nursing degree from the Catholic University of America with a specialization in community/public health nursing and the care of immigrants, refugees, and global health. She holds a certification as an advanced public health nurse (PHNA-BC). She obtained a Doctor of Philosophy (PhD) in Nursing degree from Duquesne University and completed a post-doctoral research fellowship at Dana Farber Cancer Institute and Harvard Medical School.
Her research focuses on cancer prevention and control, risk-communication, and risk-reduction. Her current work focuses on improving uptake of genetic testing for breast cancer risk (i.e., BRCA1/2 genes and multigene panel testing) through culturally appropriate interventions, to facilitate informed decision-making for cancer risk-reducing strategies, and to promote family risk communication among young breast cancer survivors and their at-risk family members, with a particular emphasis on Black and Hispanic women. Her research is supported by the National Institute of Health (NIH) and the DAISY Foundation.
Talk title: Are phenotyping algorithms fair for underrepresented minorities within older adults?
Abstract: The widespread adoption of machine learning (ML) algorithms for risk-stratification has unearthed plenty of cases of racial/ethnic biases within algorithms. When built without careful weightage and bias-proofing, ML algorithms can give wrong recommendations, thereby worsening health disparities faced by communities of color. Biases within electronic phenotyping algorithms are largely unexplored. In this work, we look at probabilistic phenotyping algorithms for clinical conditions common in vulnerable older adults: dementia, frailty, mild cognitive impairment, Alzheimer’s disease, and Parkinson’s disease. We created an experimental framework to explore racial/ethnic biases within a single healthcare system, Stanford Health Care, to fully evaluate the performance of such algorithms under different ethnicity distributions, allowing us to identify which algorithms may be biased and under what conditions. We demonstrate that these algorithms have performance (precision, recall, accuracy) variations anywhere between 3 to 30% across ethnic populations; even when not using ethnicity as an input variable. In over 1,200 model evaluations, we have identified patterns that indicate which phenotype algorithms are more susceptible to exhibiting bias for certain ethnic groups. Lastly, we present recommendations for how to discover and potentially fix these biases in the context of the five phenotypes selected for this assessment.
Bio: Dr. Juan M. Banda at his GSU lab, Panacea Lab, works on building machine learning, and NLP methods that help to generate insights from multi-modal large-scale data sources, with applications to precision medicine, medical informatics, as well as other domains. His research interests are not limited to structured data, he is also well-versed in extracting terms and clinical concepts from millions of unstructured electronic health records and using them to build predictive models (electronic phenotyping) and mine for potential multi-drug interactions (drug safety). Dr. Banda’s has published over 70 peer reviewed conference and journal papers and serves as an editorial board member of the Journal of the American Medical Informatics and Frontiers in Medicine – Translational Medicine, and a reviewer for JBI, nature Digital Medicine, nature Scientific Data, nature Protocols, PLOS One, and several other leading journals. Prior to being an assistant professor of Computer Science at Georgia State University, Dr. Banda was a postdoctoral scholar, then a research scientist at Stanford’s center of Biomedical Informatics. He is an active collaborator of the Observational Health Data Sciences and Informatics, and his work has been funded by the Department of Veteran Affairs, National Institute of Aging as well as NASA, NSF and NIH, and serves as a PC member and chair for several conferences and workshops including ICML, NeurIPS, FLAIRS, IEEE Big Data, among others.
Title: Multimorbidity Patterns Across Race/Ethnicity Stratified by Age and Obesity: A Cross-sectional Study of a National US Sample
(Due to the ongoing research, this seminar was not recorded)
Objectives: The objective of our study is to assess differences in prevalence of multimorbidity by race.
Methods: We applied the FP-growth algorithm on middle-aged and elderly cohorts stratified by race, age, and obesity level. We used 2016-2017 data from the Cerner HealthFacts® Electronic Health Record data warehouse. We identified disease combinations that are shared by all races/ethnicities, those shared by some, and those that are unique to one group for each age/obesity level.
Results: Our findings demonstrate that even after controlling for age and obesity, there are differences in multimorbidity prevalence across races. There are multimorbidity combinations distinct to some racial groups—many of which are understudied. Some multimorbidities are shared by some but not all races. African Americans presented with the most distinct multimorbidities at an earlier age.
Discussion: The identification of prevalent multimorbidity combinations amongst subpopulations provides information specific to their unique clinical needs.