This post-master’s multidisciplinary program focuses on analytical and data-driven methods for examination of the social, political, economic and technological forces that affect the organization, financing, delivery and regulation of health care services and public health systems. Students in the program acquire the interdisciplinary knowledge and skills to creatively examine complex public health challenges and health system problems. In our program, you develop a strong command of the foundation competencies of public health as well as the social determinants of health. This public health foundation will prepare you to develop the intellectual, practical, and research skills to and advance innovations in public health policy and health analytics, including health informatics, to inform the way we finance, organize, and deliver health care services and develop and implement public health policies aimed at improving the health and well-being of individuals, families, populations, and communities. The program is highly technical and requires students to use sophisticated data-driven analytical methods and tools for research.
The PhD in Health Services Research consists of a common core curriculum, concentration, and elective courses in either Health Systems and Policy or Knowledge Discovery and Health Informatics, and dissertation sequence courses. The coursework is intended to ensure that students have sufficient knowledge, background and skills needed to conduct independent novel research in the dissertation phase.
Our 72-credit hour program features two concentrations: Health Systems and Policy and Knowledge Discovery and Health Informatics.
The GRE is not required to apply.
Health Systems and Policy Concentration
Understand the structure, organization, and financing of health systems in the U.S. in order to address the social and economic determinants of health. This specialized area of study emphasizes analyses of public policies that look to address the deficiencies of our health care insurance and delivery systems as well as our public health systems to improve population health. Students develop the analytical tools and institutional knowledge to evaluate the organization, financing, and regulation of health care and public health in the United States.
Knowledge Discovery and Health Informatics Concentration
Understand advanced data analytics and big data and knowledge discovery processes to better understanding the social determinants of health and to guide decision making for the purpose of improving the health and well-being of individuals and populations. Students develop the theoretical frameworks and skills to find, extract, transform, and model data from extant databases for highly specific applications.
** Note: Please Contact a Faculty Member Before Applying **
The PhD in Health Services Research is a competitive program, and all applicants should speak with the program director or faculty member before applying. To reach a faculty member, visit the HAP directory, email email@example.com, or call (703) 993-1929.
This interdisciplinary PhD program allows students to benefit from the wide variety of faculty backgrounds and areas of expertise of the faculty within the program. More information about the research interests of the program faculty can be found on the health policy research page and the health informatics research page.
This program is producing the next generation of researchers who are solving the most complex existing and emerging health problems and threats with thoughtful analysis and evidence-based data-driven research.
Graduates are prepared to be scholars, educators, researchers, and leaders in higher education, health systems, health consulting management firms, non-government service organizations, and local, state, and federal health agencies.
Students pursuing the knowledge discovery and health informatics concentration typically focus their research on the development of analytical methods with clinical, administrative, population, and public health applications. Research areas include but are not limited to: temporal data analysis, causal inference, machine learning, agent-based modeling, artificial intelligence, biomedical ontologies, and complex data analysis.