mccullough-small

Jeffrey S. McCullough

By | | No Comments

My research focuses on technology and innovation in health care with an emphasis on information technology (IT), pharmaceuticals, and empirical methods.  Many of my studies explored the effect of electronic health record (EHR) systems on health care quality and productivity. While the short-run gains from health IT adoption may be modest, these technologies form the foundation for a health information infrastructure. We are just beginning to understand how to harness and apply medical information. This problem is complicated by the sheer complexity of medical care, the heterogeneity across patients, and the importance of treatment selection. My current work draws on methods from both machine learning and econometrics to address these issues. Current pharmaceutical studies examine the roles of consumer heterogeneity and learning about the value of products as well as the effect of direct-to-consumer advertising on health.

The marginal effects of health IT on mortality by diagnosis and deciles of severity. We study the affect of hospitals' electronic health record (EHR) systems on patient outcomes. While we observe no benefits for the average patient, mortality falls significantly for high-risk patients in all EHR-sensitive conditions. These patterns, combined findings from other analyses, suggest that EHR systems may be more effective at supporting care coordination and information management than at rules-based clinical decision support. McCullough, Parente, and Town, "Health information technology and patient outcomes: the role of information and labor coordination." RAND Journal of Economics, Vol. 47, no. 1 (Spring 2016).

The marginal effects of health IT on mortality by diagnosis and deciles of severity. We study the affect of hospitals’ electronic health record (EHR) systems on patient outcomes. While we observe no benefits for the average patient, mortality falls significantly for high-risk patients in all EHR-sensitive conditions. These patterns, combined findings from other analyses, suggest that EHR systems may be more effective at supporting care coordination and information management than at rules-based clinical decision support. McCullough, Parente, and Town, “Health information technology and patient outcomes: the role of information and labor coordination.” RAND Journal of Economics, Vol. 47, no. 1 (Spring 2016).

Greg_Rybarczyk

Greg Rybarczyk

By | | No Comments

Using GIS, visual analytics, and spatiotemporal modeling, Dr. Rybarczyk examines the utility of Big Data for gaining insight into the causal mechanisms that influence travel patterns and urban dynamics. In particular, his research sets out to provide a fuller understanding of “what” and “where” micro-scale conditions affect human sentiment and hence wayfinding ability, movement patterns, and travel mode-choices.

Recent works: Rybarczyk, G. and S. Banerjee. (2015) Visualizing active travel sentiment in an urban context, Journal of Transport and Health, 2(2): 30

lijun-small

Jun Li

By | | No Comments

Jun Li’s main research interests are empirical operations management and business analytics, with special emphases on revenue management, pricing, consumer behavior, economic and social networks. She has worked extensively with large-scale data, including transactions, pricing, inventory and capacity, consumer online search and click stream data, supply chain relationships and disruptions, clinical and healthcare claims. She is the Winner  of INFORMS Revenue Management and Pricing Practice Award for her close collaboration with retailing practitioners in implementing best response pricing algorithms. Her paper on airline pricing and consumer behavior is the finalist for Best Management Science Papers in Operations Management 2012 to 2014. She is also the principal investigator of a National Science Foundation funded project: “Gaining Visibility Into Supply Network Risks Using Large-Scale Textual Analysis”. Her work has enjoyed coverage by The Economist, New York Times and Forbes.

Supply Chain Risk Events

Supply Chain Risk Events