Healthcare Systems Research
IEOR faculty conduct research impacting different levels of the healthcare system. Current projects include research at the treatment scale (radiation treatment planning and medical robotics), at the individual scale (personalized chronic disease management and addressing food insecurity), at the infrastructure scale (operation room scheduling), and at the national policy scale.
Faculty
Selected Publications
Association of Funding and Meal Preparation Time With Nutritional Quality of Meals of Supplemental Nutritional Assistance Program Recipients
Olfat M, Laraia BA, Aswani AJ. Association of Funding and Meal Preparation Time With Nutritional Quality of Meals of Supplemental Nutritional Assistance Program Recipients. JAMA Netw Open. 2021;4(6):e2114701. doi:10.1001/jamanetworkopen.2021.14701
Applying machine learning to predict future adherence to physical activity programs
M. Zhou, Y. Fukuoka, K. Goldberg, E. Vittinghoff, and A. Aswani (2019), Applying machine learning to predict future adherence to physical activity programs, BMC Medical Informatics and Decision Making, vol. 19: 169.
Data-driven incentive design in the Medicare Shared Savings Program
Anil Aswani, Zuo-Jun Max Shen, Auyon Siddiqui, (2019), Data-Driven Incentive Design in the Medicare Shared Savings Program, INFORMS Operations Research, Vol. 67, No. 4.
Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer
Margaret P., Tyler R., Anil A., et.al. “Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer”. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1006840.
Learning 2D Surgical Camera Motion From Demonstrations
Learning 2D Surgical Camera Motion From Demonstrations. Jessica J. Ji, Sanjay Krishnan, Vatsal Patel, Danyal Fer, Ken Goldberg. IEEE International Conference on Automation Science and Engineering (CASE), Munich, Germany, August 2018.
Using telephone call rates and nurse-to-patient ratios as measures of resilient performance under high patient flow conditions
Miller, A., Aswani, A., Zhou, M. et al. Using telephone call rates and nurse-to-patient ratios as measures of resilient performance under high patient flow conditions. Cogn Tech Work 21, 225–236 (2019). https://doi.org/10.1007/s10111-018-0498-7
Superhuman Performance of Surgical Tasks by Robots using Iterative Learning from Human-Guided Demonstrations
Superhuman Performance of Surgical Tasks by Robots using Iterative Learning from Human-Guided Demonstrations. Jur van den Berg, Stephen Miller, Daniel Duckworth, Humphrey Hu, Andrew Wan, Xiao-Yu Fu, Ken Goldberg, Pieter Abbeel. IEEE International Conference on Robotics and Automation (ICRA). Anchorage, AL. May 2010.