Bottleneck analysis to improve multidisciplinary rounding process in intensive care units at Mayo clinic

Hyo Kyung Lee, Yue Dong, Brian Pickering, Ognjen Gajic, Jingshan Li

Research output: Contribution to journalArticlepeer-review


In a hospital's intensive care unit (ICU), multidisciplinary rounding (MDR) is a combination of care management with various healthcare providers from different clinical expertise meeting together to coordinate patient care, establish daily goals, and determine treatment plans. Such meetings require significant time and resource utilization from the providers. However, despite its significance, the workflow of MDR has not yet been rigorously studied. Using the data collected in ICUs at Mayo Clinic, this letter studies the MDR process by introducing a continuous time Markov chain model to systematically analyze the workflow and provide guidelines for efficiency improvement. In addition to evaluating current MDR process, a bottleneck analysis method is introduced to identify the task or activity whose improvement can lead to the largest gain in system performance. Based on the findings in bottleneck analysis, a potential MDR workflow redesign is proposed, which shifts resident's education time to a separate session out of normal rounding.

Original languageEnglish (US)
Pages (from-to)2678-2685
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number3
StatePublished - Jul 2018


  • Bottleneck analysis
  • Continuous time Markov chain (CTMC)
  • Intensive care unit (ICU)
  • Multidisciplinary rounding (MDR)
  • Workflow redesign

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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