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

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
Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions. JCO clinical cancer informatics Peterson, D. J., Ostberg, N. P., Blayney, D. W., Brooks, J. D., Hernandez-Boussard, T. 2021; 5: 1106-1126 More

Abstract

Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data.Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve.Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients.Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.

View details for DOI 10.1200/CCI.21.00116

View details for PubMedID 34752139