We are delighted to have welcomed back a former Duke Internal Medicine resident, Dr. Frank Wharam, after 17 years at Harvard Medical School Department of Population Medicine where he was the Martin Robison Delany endowed chair and Director of the Division of Health Policy and Insurance Research. As a general internist and health policy researcher, Dr. Wharam studies the impact of national and state policies on the health outcomes of chronically ill and vulnerable populations.
Dr. Wharam is a Professor of Medicine in General Internal Medicine and will be working clinically at The Duke Outpatient Clinic. He will also be a core faculty member with the Duke-Margolis Center and will be directing a new center devoted to analyzing large-scale health policy effects in order to inform a more efficient and equitable health care system.
The new center will focus on three areas:
- Scientific rigor
- Improving health outcomes through policies, especially in vulnerable populations
- Collaboration across Duke with a focus on mentorship and training the next generation
"I hope to take the findings that we have and use them to change policies, making policies more fair while we also reduce costs and improve health," says Wharam.
Wharam noted the combination of a strong medical center in combination with the Margois Center, that has a voice in Washington and across the country, was a a great benefit for a group that is trying to get research results disseminated to people who have the ability to make changes.
"We're trying to produce strong evidence that policy makers could look at, understand, and act on."
Dr. Wharam's research often examines the effects of health insurance benefit designs, such as value-based, consumer-directed, and high-deductible health plans. He also focuses on interventions that affect people with substance use disorders, diabetes, obesity, mental illness, and respiratory diseases. His other interests include the adverse effects of wasteful health care spending on medical and non-medical outcomes, with expertise in rigorous quasi-experiment research designs, causal inference in observational data, and large claims data analyses.