Research / Research AreasHealth Systems Optimization
The Center of Engineering and Health's Health Systems Optimization research focuses on the model-based approaches for safe and efficient delivery of care through operations and process engineering. Learn more about projects in this area via the links below.
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Gordon Hazen Lab
Specializing in multi-attribute decision analysis, Markov modeling, and utility modeling, as applied to medical decision making and medical cost-effectiveness.
Gordon Hazen, PhD, is an operations research professor specializing in multi-attribute decision analysis, Markov modeling, and utility modeling, as applied to medical decision-making and medical cost-effectiveness. He has conducted research in information value, parametric sensitivity analysis, probabilistic decomposition of medical Markov models, and life goal utility for medical decisions; and has applied these methods to medical concerns such as hip and knee replacement, liver transplantation, and cardiac transplant monitoring.
Professor Hazen teaches a course in medical cost-effectiveness for Northwestern’s School of Public Health. He has developed a software program for medical decision analysis, StoTree, that runs as an add-in under Microsoft Excel. He currently serves on the editorial board of Medical Decision Making, and recently completed two terms as Decision Analysis Area Editor for Operations Research.
Sanjay Mehrotra Lab
Performing advanced statistical analysis, developing large-scale optimization models, and developing detailed simulations of large, complex systems, while facilitating process and data collection to map the process.
Sanjay Mehrotra, PhD, is the director of the Center for Engineering and Health. His healthcare research includes topics in predictive modeling, budgeting, hospital operations, and policy modeling using modern operations research tools. This research involves performing advanced statistical analysis, development of large scale optimization models, developing detailed simulations of large complex systems, while facilitating process and data collection to map the process. His activities also involve training of medical professionals in healthcare quality and operations topics.
For his optimization methodology research, Mehrotra is widely known for his predictor-corrector interior point method, and methodology research in optimization that has spanned from linear, convex, mixed integer, stochastic, multi-objective, distributionally robust, and risk adjusted optimization. He was awarded the best applied paper award for the paper “Risk-adjusted budget allocation models with application in homeland security, (co-authored with Jian Hu, Tito Homem-de-Mello) published in IIE-Transactions during 2011-2012. His work is published in journals such as Mathematical Programming, SIAM Journal on Optimization, Mathematics of Operations Research, Operations Research, Optimization Methods and Software, IIE-Transactions, INFORMS Journal on Computing, Journal of Global Optimization, SIAM Journal on Computing, SIAM Journal on Numerical Analysis, Analyst, Bioinformatics, BMC BioInformatics, Annals of Surgery, and Healthcare Management Science. He has received research funding from the United States National Science Foundation, Office of Naval Research, Department of Energy, National Institute of Standards and Technology, and National Institute of Health.
Omid Nohadani Lab
Designing algorithms and solutions to health systems that account for a large variety of sources of uncertainty.
Optimization in its broad definition and robust optimization, in particular, is Professor Nohadani’s core research area. In many applications, an otherwise optimal design may perform suboptimal or even fail when uncertainties are encountered. He regards the presence of uncertainties as a natural occurrence and often an essential part of the actual phenomenon. Moreover, uncertainties can contain the story between the lines, whenever models are at best an incomplete description of the phenomenon. They may allow a deeper understanding of the underlying nature of the problem.
In Professor Nohadani research, topics in health systems were recently given a stronger emphasis due to their richness of mathematical nature, offering a plethora of optimization problems, and more importantly due to their societal impact affecting human lives. He has designed algorithms and solutions to cancer radiation therapy for a large variety of sources of uncertainty, such as tumor displacement caused by respiratory motion, tumor shape variations due to shrinkage over time, changes in radiosensitivity when chemotherapy is concurrently administered, computational errors given the underlying model uncertainties, and decision uncertainties due to planner's discretion, amongst others. Currently, he is expanding his studies to medical informatics to cope with uncertainties in data collection, categorization, and inference in the context of robust medical decision making.