A multidisciplinary approach to the development of digital twin models of critical care delivery in intensive care units

Xiang Zhong, Farnaz Babaie Sarijaloo, Aditya Prakash, Jaeyoung Park, Chanyan Huang, Amelia Barwise, Vitaly Herasevich, Ognjen Gajic, Brian Pickering, Yue Dong

Research output: Contribution to journalArticlepeer-review


To investigate critical care delivery in intensive care units (ICUs), we propose a qualitative and quantitative coupling approach to developing an ICU digital twin model. The Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model was adapted to conceptualise the current ICU system. A hybrid simulation model was developed to characterise major care delivery processes as discrete-time events, feature patients, clinicians, and other artifacts as autonomous agents, and integrate them in the same simulation environment to capture their interactions under a variety of ICU production conditions. Electronic health record (EHR) data from a medical ICU of Mayo Clinic Rochester, Minnesota, were used to calibrate model parameters. Upon iterative refinement and validation, the model has the potential to be integrated with the hospital information system to simulate real-life events as a full-fledged digital twin of the system. It can be used as an in-silico testbed to investigate the real-time allocation of ICU resources such as medical equipment, flexible staffing, workflow change, and support decisions of patient admission, discharge, and transfer, for healthcare delivery innovation. The interdisciplinary nature of this framework demonstrates and promotes the partnership between healthcare and engineering communities to building a better delivery system.

Original languageEnglish (US)
Pages (from-to)4197-4213
Number of pages17
JournalInternational Journal of Production Research
Issue number13
StatePublished - 2022


  • Systems engineering
  • critical care
  • digital twin
  • hybrid simulation
  • patient safety

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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