Principal Investigator

Associate Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health

(650) 725-5507

boussard@stanford.edu

Bio

Dr. Hernandez-Boussard is an Associate Professor at Stanford University in Medicine (Biomedical Informatics), Biomedical Data Sciences, Surgery and Epidemiology & Population Health (by courtesy). Her background and expertise are in the field of biomedical informatics, health services research, and epidemiology. In her current work, Dr. Hernandez-Boussard develops and evaluates AI technology to accurately and efficiently monitor, measure, and predict healthcare outcomes. She has developed the infrastructure to efficiently capture heterogenous data sources, transform these diverse data to knowledge, and use this knowledge to improve patient outcomes, healthcare delivery, and guide policy.

Publications

The AI life cycle: a holistic approach to creating ethical AI for health decisions. Nature medicine Ng, M. Y., Kapur, S., Blizinsky, K. D., Hernandez-Boussard, T. 2022 More

View details for DOI 10.1038/s41591-022-01993-y

View details for PubMedID 36163298
Picture a data scientist: a call to action for increasing diversity, equity, and inclusion in the age of AI. Journal of the American Medical Informatics Association : JAMIA de Hond, A. A., van Buchem, M. M., Hernandez-Boussard, T. 2022 More

Abstract

The lack of diversity, equity, and inclusion continues to hamper the artificial intelligence (AI) field and is especially problematic for healthcare applications. In this article, we expand on the need for diversity, equity, and inclusion, specifically focusing on the composition of AI teams. We call to action leaders at all levels to make team inclusivity and diversity the centerpieces of AI development, not the afterthought. These recommendations take into consideration mitigation at several levels, including outreach programs at the local level, diversity statements at the academic level, and regulatory steps at the federal level.

View details for DOI 10.1093/jamia/ocac156

View details for PubMedID 36048021
Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model SCIENTIFIC DATA Roosli, E., Bozkurt, S., Hernandez-Boussard, T. 2022; 9 (1): 24 More

Abstract

As artificial intelligence (AI) makes continuous progress to improve quality of care for some patients by leveraging ever increasing amounts of digital health data, others are left behind. Empirical evaluation studies are required to keep biased AI models from reinforcing systemic health disparities faced by minority populations through dangerous feedback loops. The aim of this study is to raise broad awareness of the pervasive challenges around bias and fairness in risk prediction models. We performed a case study on a MIMIC-trained benchmarking model using a broadly applicable fairness and generalizability assessment framework. While open-science benchmarks are crucial to overcome many study limitations today, this case study revealed a strong class imbalance problem as well as fairness concerns for Black and publicly insured ICU patients. Therefore, we advocate for the widespread use of comprehensive fairness and performance assessment frameworks to effectively monitor and validate benchmark pipelines built on open data resources.

View details for DOI 10.1038/s41597-021-01110-7

View details for Web of Science ID 000746595100001

View details for PubMedID 35075160
Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nature medicine Hernandez-Boussard, T., Macklin, P., Greenspan, E. J., Gryshuk, A. L., Stahlberg, E., Syeda-Mahmood, T., Shmulevich, I. 2021 More

View details for DOI 10.1038/s41591-021-01558-5

View details for PubMedID 34824458