Our team uses large-scale real-world data and rigorous analytic methods to generate actionable evidence that improves care, outcomes, and equity for high-need, high-cost populations.
My research is dedicated to improving the value of healthcare and mitigating disparities for high-need, high-cost patients. While they represent a small subset of patients overall, high-need, high-cost patients drive the majority of U.S. healthcare spending and consistently experience quality and safety issues. As a result, healthcare system leaders, payers, and policymakers prioritize this group.
A hallmark of my work is the application of advanced quantitative methods to large-scale healthcare data to characterize needs and evaluate policy solutions to improve care and outcomes in these populations. My contributions are highlighted in three interconnected domains: (1) identifying, characterizing, and predicting high-need, high-cost patients, defined as patients with unmet healthcare needs in the top segment of healthcare spending; (2) examining how social determinants of health (SDoH) contribute to healthcare utilization and studying strategies to address social risk factors for poor health and health outcomes; and (3) policy solutions for high-need, high-cost patients.
My scientific impact is demonstrated by a consistent track record of competitive funding, with seven total grants as PI from the NIA, NIMH, and private foundations, as well as over 50 peer-reviewed publications in leading health services, clinical, and policy journals. Critically, this work has translated into real-world impact, informing national policy to screen and adjust for social risk factors in Medicare payment, shaping population health initiatives to reduce cardiovascular disease burden in New York City, and improving care coordination for high-need, high-cost patients in health systems.
I have demonstrated national and international leadership through my engagement with major research funders, scholarly journals, and policy initiatives. I have served on five NIH study sections and as an expert reviewer for the National Medical Research Council, the national funding agency under Singapore’s Ministry of Health. I serve as an editorial board member for Health Services Research, the leading journal in my field. In 2024, I served as a reviewer for the National Academy of Medicine report Long-Term Health Effects of COVID-19, supporting evidence synthesis on a major emerging public health challenge. As a member of the AcademyHealth Long-Term Services and Supports (LTSS) Interest Group Advisory Committee, I led organization of research panels focused on American Rescue Plan Act-funded priorities, connecting investigators and policymakers to accelerate evidence generation for long-term care reform. Finally, as a member of INSIGHT Clinical Research Network AI in Health Research Accelerator Team, I am leading cross-institutional collaborations to advance the application of AI to study patient-centered outcomes.
Biography
Dr. Yongkang Zhang is an Assistant Professor in the Department of Population Health Sciences at Weill Cornell Medicine. His research focuses on using large-scale healthcare datasets and advanced quantitative methods to improve care for high-need, high-cost patients. His recent projects examine end-of-life care, social determinants of health, and Medicare Advantage plans. By combining rigorous analytics with real-world data, he aims to identify practical solutions to improve outcomes and reduce disparities. Dr. Zhang's research has been supported by NIA, NIMH, PCORI, and private foundations.
Distinctions:
Real World Data Scholar, Long-Term Care (LTC) Data Cooperative of the NIA IMPACT Collaboratory
NIA AWARD Network Summer Institute Scholar
Delta Omega National Public Health Honor Society
Nobuo Maeda International Research Award, American Public Health Association
