معرفی
Leila Agha is an Associate Professor of Health Care Policy at Harvard Medical School, Harvard University, and a Research Associate at the National Bureau of Economic Research (NBER). She serves as co-editor of the Journal of Health Economics and associate editor of Management Science.
Her academic journey includes an S.B. and Ph.D. in economics from MIT. Her research investigates how productivity in the US health care system is shaped by innovation, technology, and the organization of work, with a focus on determinants of healthcare innovation, care fragmentation, and provider team dynamics.
Dr. Agha's work spans health economics, organizational economics, and labor economics, examining critical issues such as pharmaceutical innovation under insurance constraints, physician adoption of new technologies, peer network effects on drug diffusion, and the impact of work disruptions on patient care. Her methodological approach leverages large-scale administrative datasets including Medicaid and Medicare claims to analyze real-world healthcare delivery and outcomes.
Her publication record reveals consistent contributions to understanding healthcare coordination, with recurring themes of organizational boundaries, guideline adherence, and efficiency measurement across 10+ major works from 2014-2025. Key trends include increasing focus on machine learning applications in emergency departments (HEARTSPOT project) and gender-specific labor supply shocks in healthcare.
Research funding includes significant grants such as Provider Organizations and Care Coordination: Effects on Utilization, Quality and Outcomes and HEARTSPOT: Implementation and Rigorous Evaluation of a Machine Learning System to Aid Decision-Making in the Emergency Department. While specific advisees aren't listed, her collaborative work involves multidisciplinary teams across economics, medicine, and data science.
Her laboratory approach centers on healthcare referral networks and provider team analysis, utilizing natural experiments and quasi-experimental designs to isolate causal effects in complex healthcare systems.
