AI in Medicine: Will It Improve Or Worsen Health Care Disparities?

CONTROVERSIES IN POPULATION HEALTH

AI in Medicine: Will It Improve Or Worsen Health Care Disparities?

Thursday, October 5, 2023   

12 – 1:30pm EST    

Virtual Event  

Register

Moderator

Rumi Chunara, PhD

Associate Professor of Biostatistics, NYU School of Global Public Health
Associate Professor of Computer Science and Engineering, NYU Tandon School of Engineering

Director, Center for Health Data Science

The overarching goal of Dr. Rumi Chunara’s research is to develop computational and statistical approaches for acquiring, integrating and using data to improve population-level public health. She focuses on the design and development of data mining and machine learning methods to address challenges related to data and goals of public health, as well as fairness and ethics in the design and use of data and algorithms embedded in social systems. At NYU, Dr. Chunara also directs the Center for Health Data Science, which develops data science methods in public and population health, as well as knowledge ecosystems to share and translate knowledge across disciplines and places. Previously, she was a Postdoctoral Fellow and Instructor at HealthMap and the Children’s Hospital Informatics Program at Harvard Medical School. She completed her PhD at the Harvard-MIT Division of Health Sciences and Technology and BSc at Caltech.

Panelists

Yindalon Aphinyanaphongs, MD, PhD

Assistant Professor of Population Health and Medicine

NYU Grossman School of Medicine

Dr. Yindalon Aphinyanaphongs is a physician scientist in the Center for Healthcare Innovation and Delivery Science at NYU Langone Medical Center. His lab focuses on novel applications of machine learning to clinical problems and research and infrastructure regarding model deployment and maintenance. Operationally, he is the Director of Operational Data Science and Machine Learning. In this role, he leads a Predictive Analytics Unit composed of data scientists and engineers that build, evaluate, benchmark, and deploy (i.e. translate) predictive algorithms into the clinical enterprise.

Kenrick Cato, PhD, RN, CPHIMS, FAAN

Professor of Nursing / Nurse Scientist, Pediatric Data and Analytics

University of Pennsylvania / Children’s Hospital of Philadelphia

Dr. Kenrick Cato is a clinical informatician whose research focuses on mining electronic patient data to support decision-making for clinicians, patients, and caregivers. Operationally, he spends his time mining and modeling Nursing data to optimize Nursing value in Healthcare. He is also involved in several national-level informatics organizations, including as a board member of the American Medical Informatics Association (AMIA), Chair of the Nursing Informatics Working Group (NIWG) of AMIA, as well as a convening member of the AMIA-sponsored 25 x 5 initiative to reduce documentation burden. Dr. Cato received his BSN, MS, and Ph.D. in Clinical informatics at Columbia University.

Charlene Ngamwajasat, MD

Senior Physician Informaticist and Population Health Clinical Advisor

Bureau of Equitable Health Systems, NYC Department of Health and Mental Hygiene

Dr. Charlene Ngamwajasat serves as the Senior Physician Informaticist and Population Health Clinical Advisor for the Bureau of Equitable Health Systems at the NYC Department of Health and Mental Hygiene. She leads clinical quality improvement work, supports strategic planning for population health initiatives, implements informatics projects and provides interprofessional trainings. Dr. Ngamwajasat is a graduate of the Sophie Davis School of Biomedical Education, received her MD from SUNY Downstate, and completed her internal medicine residency at Lenox Hill Hospital. She is also board certified in clinical informatics.

Kellie Owens, PhD

Assistant Professor of Population Health

NYU Grossman School of Medicine

Dr. Kellie Owens is a medical sociologist and empirical bioethicist whose work focuses on the social and ethical implications of health information technologies. She is particularly interested in understanding when and how new technologies improve or worsen health inequities. Her most recent projects seek to develop better social and technical infrastructure to support artificial intelligence and machine learning (AI/ML) tools in healthcare, and explore the ethical use of genomic data. Her work is supported by the National Human Genome Research Institute and has received awards from the American Sociological Association, the American Anthropological As