Faculty Spotlight - Dr. Greg Lubiani

Q&A with Greg Lubiani, PhD

Division Director and Associate Professor

1. What brought you to the UofM School of Public Health? 
   I was born and raised in Memphis and, as a University of Memphis graduate, the opportunity to return home and serve the institution that helped shape my career was especially meaningful. I was also excited to join a Division and School of Public Health with a strong history and considerable potential for continued growth. This position offers the chance to build on that foundation, strengthen partnerships across the university and with healthcare organizations, alumni, and community partners, expand opportunities for students and faculty, and help take the MHA program and the Division to new heights.
   
2. What is the broad focus of your research? 
   My research is focused around health economics, health policy, and artificial intelligence. I use applied microeconomics and quantitative methods to study how incentives, prices, organizational structures, and public policy affect healthcare costs, utilization, productivity, access, and outcomes. My work has expanded to examine decision-making and risk in AI-enabled settings and illustrate how AI can support healthcare and public health organizations in ways that are ethical and responsible while adding value for stakeholders.
   
3. What inspired you to pursue this particular area of research? 
   My interest in health economics grew out of experience inside healthcare organizations. As a senior operations analyst, I evaluated productivity and profitability across more than 1,200 therapists and 600 clinics, analyzed payer contracts, and supported operational initiatives. I later studied advanced health economics research methods as both a PhD student and Research Associate at the Methodist Le Bonheur Center for Healthcare Economics. Those experiences showed me how choices about resources, incentives, and policy have direct consequences for organizations, patients, and communities. 
   
4. What is the most exciting project you are currently working on? 
   I’m especially excited about my current research on the utilization of generative AI in healthcare and public health. For example, one project asks whether large language models can help reduce food insecurity. More broadly, I want to understand where AI can improve access to information and decision support, where it may introduce new risks or inequities, and how its value should be measured. 
   
5. How does your research impact or benefit the broader community or public health field? And how do you envision your research evolving in the next few years? 
   Health economics can help organizations and policymakers make better use of limited resources. My work on healthcare efficiency, productivity, utilization, expenditures, and financial distress is intended to inform decisions that affect access, affordability, quality, and population health. Over the next several years, I hope to build more partnership-based research with colleagues across academia and with health systems, public agencies, and community organizations.
   
6. What is the coolest training or program you've been a part of, or your favorite conference you've attended?   
   The Post Graduate Program in Artificial Intelligence and Machine Learning at the University of Texas at Austin has been the most useful recent training experience for me. It gave me a structured technical foundation in machine learning while I was developing research questions at the intersection of economics, healthcare, and AI. It also pushed me to think more carefully about what these technologies and methods can do, why some users may struggle getting the full value from them, and how their performance could be evaluated. 
7. What is your favorite self-authored manuscript? 
   My current favorite is “Revealing Risk Preferences Through AI Prompting Effort,” published in the Journal of Risk and Financial Management in 2026 with Drs. Brian Toney and Albert Okunade. The research provides the first connection of how an individual views risk to how they utilize AI platforms. Connecting economic theory and behavioral measurement with an emerging AI setting, it reflects the direction I want more of my research to take by using established economic ideas to ask novel questions about operations and behavior in new technological environments.
   I would also highlight “Quality-Quantity Decomposition of Income Elasticity of U.S. Hospital Care Expenditure Using State-Level Panel Data,” published in Health Economics with Weiwei Chen and Albert Okunade. In that paper, we developed a new method for separating the quality and quantity components of the income elasticity of hospital-care expenditures. Defining quality in service sectors is inherently difficult, and healthcare quality measures are often incomplete or limited to a narrow set of indicators. Our approach avoids requiring a single explicit definition of quality. Instead, it backs into a broader, all-encompassing quality measure, allowing us to examine how rising income affects both the quantity and quality of hospital care demanded.
   
8. What kind of research would you like to be doing that you haven't yet had the opportunity to do? 
   II think it’s important to collaborate on research projects across disciplines, now more than ever. Healthcare and AI are both multi-disciplinary by nature, so I’m really looking forward to expanding my research through continued collaborations with colleagues across areas of specialization, institutions, and geographic locations. These partnerships help to produce not only higher quality research, but also important questions that have not yet been asked.
   
9. Are there any publications, awards, or recognitions you would like us to include in the spotlight? 
   Recent work that may be useful to highlight includes “Revealing Risk Preferences Through AI Prompting Effort” in the Journal of Risk and Financial Management (2026); “Hospital Efficiency and Productivity: Bias-Corrected Evidence from Control-Function Estimation,” in the International Journal of Productivity and Performance Management (2026); and “Determinants Of COVID 19 Mortality Among US Law Enforcement Officers” in the Journal of Population Research (2025). My earlier healthcare research has appeared in Health Economics, Managerial and Decision Economics, and Applied Economics Letters. I also serve as a Guest Editor for PLOS ONE and a Review Editor for Frontiers in Public Health.