IEOR - Designing a More Efficient World

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

Anil Aswani

Associate Professor

Ken Goldberg

Professor

Xin Guo

Professor

Rhonda Righter

Professor
ORMS Advisor

Selected Publications

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.

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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.

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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.

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Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning

Liangyuan Na, Cong Yang, Chi-Cheng Lo, Fangyuan Zhao, Yoshimi Fukuoka, Anil Aswani. “Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning”. JAMA Netw Open. 2018;1(8):e186040. doi:10.1001/jamanetworkopen.2018.6040. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2719130

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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.

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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 21225–236 (2019). https://doi.org/10.1007/s10111-018-0498-7

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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.

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