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.
Cheryl Clark, Assistant Professor of Medicine, Harvard Medical School (Apr. 21, 12 pm ET)
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.
Tarsha Jones, Assistant Professor of Nursing, Florida Atlantic University (Apr. 20, 12 pm ET)
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.
Juan Banda, Assistant Professor of Computer Science, Georgia State University
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.
Charisse Madlock-Brown, Assistant Professor, University of Tennessee Health Science Center
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.
Title: Identifying and Leveraging Public Data Sources with Social Determinants of Health Information for Population Health Informatics Research
Abstract: Social determinants of health (SDOH) account for many health inequities. Data sources traditionally used in informatics research often lack SDOH, and, when available, SDOH may be difficult to leverage given it’s lack of specificity and lack of structured information. In this presentation, I will share the initial phases of work that we are doing around leveraging SDoH data – for health equity research – addressing some of the informatics challenges leveraging social determinants of health data to inform population health or inform health services research. I will discuss a case study using a machine learning clustering algorithm to uncover region-specific sociodemographic features and disease-risk prevalence correlated with COVID-19 mortality during the early accelerated phase of community spread.
Megan Threats, PhD, MSLIS, Assistant Professor of Library and Information Sciences