News and Stories

These are articles and news stories on the work Research IT has been doing over the years. Please visit us again to read the new articles we will be posting in the future.

  • Research IT COVID-19 project

    Feb 11, 2021:TDS data experts, Jose Posada (Research IT) and Gomathi Krishnan (Analytics) partnered with Stanford physicians and data scientists to find a way to boost ICU capacity by as much as 25%.

  • STARR Informatics Summit 2021

    Priya Desai, R&D Manager Biomedical Informatics, Research IT, Innovation and Translation (IaT) at TDS, and Nigam Shah, Professor of Biomedical Informatics, and Data Science at IaT are inviting you to the first ever STARR informatics summit.

  • ResearchIT-2020-year-in-review

    Dec 15, 2020:With shelter-in-place, research pace hasn't reduced. If anything, COVID-19 may have accelerated the pace of research and increased the sense of urgency.

  • Stanford Cybersecurity and Privacy Festival 2020

    Oct 30, 2020:Somalee Datta, PhD, Director of Research IT, presented on state-of-the-art at Stanford and elsewhere in privacy preserving techniques for secondary use of patient data in biomedical research.

  • International COVID19 Network Study

    Oct 30, 2020:Our new research CDW, STARR-OMOP, launched an year ago, was used in a COVID-19 characterization study and published in Nature Communications.

  • Medication app in Epic Hyperspace

    Oct 30, 2020: Heparinā€induced thrombocytopenia (HIT) and Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS) are two immune mediated adverse drug reactions that can be devastating if not properly identified.

  • OHDSI-2020-Symposium

    Oct 30, 2020: Jose Posada, Ph. D., Sr. Clinical Data Scientist from Research IT and a member of TDS Data Science team presented on Research IT's clinical text de-identification method, TiDE at the symposium collaborator showcase.

  • STARR-Radiology manuscript on arXiv

    Aug 9, 2020:With the increase in Artificial Intelligence driven approaches, researchers are requesting unprecedented volumes of medical imaging data which far exceed the capacity of traditional on-premise client-server approaches for making the data research analysis-ready.