Donald Lee
Associate Professor of Information Systems & Operations Management | Emory University, Goizueta Business School
Atlanta, GA, UNITED STATES
Links (2)
Biography
Professor Lee's research develops rigorous data science techniques for improving the delivery of health care. His work is recognized by R01 funding from the National Institutes of Health, one of the nation's preeminent grants for medical research. On the applied front, he has extensive experience designing data-driven tools for problems ranging from healthcare financial planning to real-time warning systems for adverse medical events. On the methodological front, his research has resolved foundational questions in causal inference and in survival machine learning. His work has appeared in leading journals in management, statistical machine learning, and healthcare.
Professor Lee also holds a joint appointment in the Department of Biostatistics & Bioinformatics at the Rollins School of Public Health. Prior to joining Emory, he served as an associate professor at Yale and held appointments in the School of Management and in the Department of Statistics & Data Science.
Education (2)
Stanford University: MS/PhD (Statistics/Operations Research)
Cambridge University: BA/MA/MMath (Mathematics)
Areas of Expertise (4)
Healthcare Operations
Medical outcomes evaluation
Statistical machine learning
Causal Inference
Research Spotlight
In the News (1)
Hiring More Nurses Generates Revenue for Hospitals
EmoryBusiness.com online
2024-09-05
But might the balance sheet in fact be skewed in some way? Could there be potential financial losses attached to nurse understaffing that administrators should factor into their hiring and remuneration decisions? Research by Goizueta Professors Diwas KC and Donald Lee, as well as recent Goizueta PhD graduates Hao Ding 24PhD (Auburn University) and Sokol Tushe 23PhD (Muma College of Business), would suggest there are. Their new peer-reviewed publication* finds that increasing a single nurse’s workload by just one patient creates a 17% service slowdown for all other patients under that nurse’s care.
