Contact Information

Welcome to Biomedical Informatics at USU


Uniformed Services University of the Health Sciences
Biomedical Informatics
4301 Jones Bridge Road
Bethesda, Maryland 20814-4799
Room G058K
(301) 295-9402 (phone)
(301) 295-3585 (fax)

harry.burke@usuhs.edu

Harry B. Burke, M.D., Ph.D.

Associate Professor

Specialty

Internal Medicine

Research Interests

I. Safety and Quality Improvement

  • Providing tools for the "bottom-up" detection of safety issues and for improving quality, i.e., clinicians analyze their patient populations and discover ways to improve their safety and quality of care.
  • Creating system-related projects that provide clinicians and patients with information designed to increase safety and improve quality of care. For example, providing them with a patient's cumulative lifetime radiation exposure.
  • Creating iPad-based patient monitoring, intervention, and information delivery systems to reduce hospital readmissions.

II. Medical Decision Making and Clinical Decision Support Systems

  • Developing clinical decision support systems (CDSS) that provide clinicians and their patients with evidence-based information to assist in their decision making and integrating these systems into the practice of medicine through their incorporation into the iEHR.
  • Understanding how clinicians and patients make decisions so that we can optimize the interaction between clinicians and CDSS.
  • Integrating personalized medicine in the informed decision making process. Patient-specific, evidence-based predictions (personalized medicine) can be integrated into CDSS to assist clinicians and patients in their decision-making.

III. Improving Medical Quantitative Methods

  • Science is based on comparisons. I am interested in creating an efficient and accurate quantitative method for comparing two or more sets of categories in terms of the frequency of co-occurrence of entities between the sets of categories.
  • Using information-theoretic mathematics to model healthcare systems.
  • Predictive medicine is critical to personalized medicine. Non-procedural medicine is based on risk, diagnostic, and prognostic predictions, i.e., the patient is at risk for disease, the patient has the disease, and the patient will benefit from the treatment. I am interested in developing new methods for predicting patient outcomes.