1. Proceedings
  2. I3M
  3. 2021
  4. IWISH
  5. 10.46354/i3m.2021.iwish.013

Using discrete-event simulation to address COVID-19 health and safety guidelines in outpatient laboratory clinic

  • a Daniel Zemaitis , 
  • b Angela Green , 
  • c Alexandra Mukavitz , 
  • d Jennifer Dhanapal , 
  • e Myriah Kahlmorgan , 
  • f Edward J. Williams  
  • a,b,c,d,e,f University of Michigan – Dearborn, 19000 Hubbard Drive, Dearborn, Michigan, 48126, USA
Cite as
Zemaitis D., Green A., Mukavitz A., Dhanapal J., Kahlmorgan M., Williams E.J. (2021). Using discrete-event simulation to address COVID-19 health and safety guidelines in outpatient laboratory clinic. Proceedings of the 10th International Workshop on Innovative Simulation for Healthcare (IWISH 2021), pp. 84-89. DOI: https://doi.org/10.46354/i3m.2021.iwish.013

Abstract

Simulation has, over multiple decades, achieved a remarkable record of improving operational efficiency and effectiveness in many areas – manufacturing, supply chains (including commercial transportation and logistics), health care, public-sector transport, service industries, and military operations.  About ⅔ through the twentieth century, simulation’s earliest successes appeared in the manufacturing sector.  These successes began with attention to value-added operations (e.g., at machines often entailing high capital investments) and rapidly spread to the non-value-added but very necessary material-handling requirements within factories.  SARS-CoV-2, (COVID-19) has caused a rapid, widespread change in patient care across the globe.  New health and safety guidelines have been established by the Centers for Disease Control and Prevention (CDC) (Health Care Guidelines, 2020).  Still, it has been left to individual facilities to address and implement solutions to new standards for social distancing and cleanliness.  Here we develop a discrete-event simulation model to simulate an outpatient laboratory clinic, including check-in and patient interaction, to determine if changes lead to increased efficiency and reduce patient wait times, without increasing staffing or additional resources.  Under the aegis of the University of Michigan Medical Group (UMMG), this simulation is validated against real data of waiting time at the University of Michigan Canton Health Center (UMCHC) during the height of the pandemic.

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