
Digitally Driven
The Vision of Large-Scale Computing and Data Analytics— Untangling the Complexity of Health
There’s power and impact in large-scale computing and data analysis. Through the Stanford Biomedical Data Science Initiative, we are delving into every aspect of health and disease, from the molecular mechanics of individual cells to the behavioral dynamics of entire populations.
Our vision is to integrate genomics and other “omic” data into everyday care by adding it to medical records to deepen our understanding of a patient’s biology.
Already, we’re using mobile data to give physicians and researchers real-time information that evolves over time for patients.
Recently, we have begun nurturing a new breed of scientist who will amplify the potential of biomedical data—experts with the talent and training to work at the intersection of the life and quantitative sciences.
COMBINING DATA
DEVELOPING INTELLIGENT SYSTEMS
MAKING IT ALL SEARCHABLE IN REAL TIME
We’re combining different types of data—clinical, immunological, social, environmental, etc.—to create a medical data set of unprecedented breadth, depth, scale, and dynamism. And we’re building tools and writing algorithms to mine this data for fresh insights.
We’re developing intelligent systems that can quickly analyze vast amounts of unstructured data like images and physicians’ notes. We’re integrating it all to give us the most detailed and comprehensive picture of human health ever seen.
We’re making it all searchable in real time, so physicians and researchers can have accurate outcomes information on huge numbers of patients just like the ones they’re treating or studying, right at their fingertips.
DIGITALLY DRIVEN: CONNECTING TECHNOLOGY AND BIG DATA, FROM EDUCATION TO PRACTICE
We're building the infrastructure, the resources, and the right collaborations to make digital health a reality for patients. Why? Because we see data permeating every component of the health care ecosystem—medical research, daily life, the patient experience, ongoing care, prediction, and prevention.