Health Services Research, PhD

Contact Info

Please visit our PhD Program FAQs. Additional information is available in our department’s FAQs for prospective students. If you still have unanswered questions, please email us at

Program Overview

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. Students in the program acquire the interdisciplinary knowledge and skills to creatively research complex health and health system problems. In our program, you will identify and develop innovations in health policy and health analytics, including health informatics, to inform the way we finance, organize, and deliver health care services for individuals, 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 the U.S. health care system in order to assess the social and economic factors underlying population health. This specialized area of study emphasizes analyses of public policies that seek to address the deficiencies of our health care insurance and delivery systems and to improve population health.  Students develop the analytical tools and institutional knowledge to evaluate the organization, financing, and regulation of health care delivery in the United States. 

Knowledge Discovery and Health Informatics Concentration

Understand advanced data analytics and data complexity, as well as information generation, processing, and use for clinical, administrative, and research purposes. Students focus on development and advanced use of data science and other analytical methods applied in clinical, administrative or population-level settings.

** Note: Please Contact a Faculty Member Before Applying **

The PhD in Health Services Research is a competitive program, and all applicants should speak with a faculty member before applying. To reach a faculty member, visit the HAP directory, email, 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.

>> Review the Application checklist before applying.


This program will produce the next generation of researchers who can tackle and solve the complex problems in public and private health systems with thoughtful analysis and evidence-based data-driven research.

Graduates are prepared to be scholars, educators, researchers, and leaders in higher education, health care and service organizations, health care consulting firms, government and nonprofit organizations, and private businesses that support or regulate the health service industry.

Students pursuing the knowledge discovery and health informatics concentration typically focus their research on the development of analytical methods with clinical, administrative and population 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.