Latest information on COVID-19


COVID-19 Research by BMIR

The Stanford Center for Biomedical Informatics Research (BMIR) is leading data science efforts to address the COVID-19 pandemic—to enhance patient care, to measure the scale of the infection, and to model the demand on healthcare resources.

Many scientists within BMIR are involved, including Mark Musen, Manisha Desai, Tina Hernandez-Boussard, and Nigam Shah. Shah is shown here in a recent video from the Department of Medicine Grand Rounds, where he discusses modeling for COVID-19 planning and response. See video below.

Updated COVID Research by BMIR

In this unprecedented situation, BMIR faculty members are working hard to apply their skills to address the COVID-19 pandemic.

There is a lot of excitement for using data science and modeling to forecast how COVID-19 will spread and affect their communities, and BMIR Biomedical Informatics researchers are at the forefront of that work. 

Jonathan Chen, for example, is using Stanford electronic health record (EHR) data to help identify trends in COVID-19 data of importance to the CDC.  Nigam Shah is leading Stanford’s effort to mine EHR data to identify and predict changing requirements for resource utilization.  He also is assisting colleagues in developing better models of the pandemic based on more granular and more accurate input data. Another area of activity involves continuously profiling the patients screened for SARS-CoV-2 in our health system. Nigam Shah is heading those efforts.

Manisha Desai has pivoted our BMIR Quantitative Sciences Unit to launch new clinical trials as well as observational studies and quality improvement work.  She and Tina Hernandez-Boussard are collaborating on two other projects: predicting best strategies for scarce resource allocation during the COVID-19 pandemic and modeling outcomes of COVID-19 patients.

BMIR Focus

The Stanford Center for Biomedical Informatics Research (BMIR) uses advanced research techniques to discover, apply, translate, and organize data that make a difference for health and healthcare. With its expertise in clinical and translational informatics research and biostatistics, the division works to uncover new ways to advance personalized medicine and to enhance human health and wellness. 

Collaboration is in our DNA. We are excited about the prospect of working with other experts who share our goal to connect data to health and medicine. We encourage you to contact Mark Musen, Director of BMIR ( to learn more about the Stanford Center for Biomedical Informatics Research. 

Also please join us at an upcoming BMIR Center for Biomedical Informatics Research Colloquium.

Explore BMIR

At BMIR, we develop computational methods for biomedical discovery that influence medical decisions.           

Learn more about the cutting-edge ways we are advancing technology and biomedicine to improve human health.

Our state of the art research advances patient care by improving semantic technology, biostatistics, and the modeling of biomedical systems. Read more about our research labs.

Join us for our weekly research talks featuring world-renowned scientists, faculty, staff, and students.                           

BMIR Colloquia and Research in Progress talks occur on Thursdays from 12-1 PM during the academic year in Medical School Office Building room X275, 1265 Welch Road, Stanford, CA. See schedule.

Notable Projects and Services

CEDAR is making data submission smarter and faster, so biomedical researchers and analysts create and use better metadata.


The NCBO manages a repository of all the world’s publicly available biomedical ontologies and terminologies—now more than 390 in number.

Protégé is the most widely used ontology-development system in the world.

Diagnostics - Infectious Diseases

EteRNA, an online puzzle, enlists video gamers to try to design a sensor module that could make diagnosing TB as easy as taking a home pregnancy test. Learn more at  Can An Online Game Help Create A Better Test For Tuberculosis?

Green button: the promise of personalizing medical practice guidelines in real time.

CoINcIDE, is a novel methodological framework for the discovery of patient subtypes across multiple datasets that requires no between dataset transformations.

Learn more at CoINcIDE: All together now