We Connect Data to Health
BMIR conducts research to develop biomedical informatics methods to discover insights and relationships between health and data. We connect data to health to improve clinical practice, to influence healthcare policy, to enhance the administration of health care, and to enable new advances in the diagnosis and treatment of disease.
BMIR seeks practical applications to complex problems.
With the advent of the big data revolution, there is a critical need for scientists with skills at the interface of data and analysis, those who will structure, organize, and find meaning in large datasets, who understand and apply data insights to clinical care and biomedical problems, and who can conduct data-driven translational research. Our focus on applications and practical results is necessary for bringing new practices to health systems and research laboratories, and vital for discovering new pathways for improving patient care and health outcomes.
We are a diverse group of experts in biomedical informatics from all around the world.
Our faculty, research staff, and trainees are advancing clinical informatics, translational informatics, and biostatistics.
BMIR seeks to improve health and wellness through biomedical discovery and enhanced clinical care using data, information, and computation. As a collaborative team, we:
- Strive to be the premier organization for the development and evaluation of computational methods for discovery and decision making in biomedicine.
- Promote and foster an environment that integrates research, training, and adoption of biomedical information technology.
- Connect the clinical enterprise and the basic sciences to enhance patient care and to provide a laboratory for informatics.
Message from the Director
"It is a critically important time to make a difference in health and healthcare. The opportunities for informatics and quantitative analytic methods to achieve breakthroughs in clinical care and translational science have never been greater.
— Mark Musen, Director, Stanford Center for Biomedical Informatics Research