May 19, 2022By Lance Baily

Research: Improving Healthcare Through Discrete-Event Simulation During COVID-19

Healthcare simulation research is an important way for community leaders to work toward improving the industry overall. One facet of the clinical simulation industry where research has been especially necessary to determine best practices is that of discrete event simulation across healthcare (specifically during the COVID-19 pandemic). A recent article, written by an AI developer and a heavy writer, Kartikeya Rana, highlighted a literature review of eight research papers and specified how discrete-event simulation can be used to improve healthcare simulation in both educational and training settings.

Beginning his article, Rana shares how, over the past 20 years, there has been an increase in clinical simulation modeling to improve and optimize the various processes. These processes include medical staff scheduling, resource allocation, patient admission, staff-patient ratio, and the requirement of inpatient beds. However, he notes that COVID-19 created the need for healthcare institutions to treat higher volumes of patients with limited staff and resources. As the number of patients grew, and resources dwindled, immense pressure mounted.

Under these scenarios, discrete event simulation modeling helped assess process flows including laboratory saliva testing, hospital resource allocation between covid and non-covid patients, patient drive-through testing, and many more. As real-time experience is not always possible, these healthcare simulation experiences provided critical training and insight to learners and professionals treating COVID-19 patients. Additionally, clinical simulation models can help analyze their situation related to COVID-19 and provide recommendations and improvement points.

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Literature Review Details

There were several key takeaways from this literature review, one of which was that Qureshi, et al. (2019) developed a simulation model using Rockwell ARENA to “analyze the effect of the nurse-patient ratio on the nurse workload and patient care quality.” Rana reports that the authors used Microsoft Vision software to design the floor plan details of a caring unit (such as the nursing station and the number of beds). The data for healthcare simulation was obtained from a large urban academic health care center in Canada for a month, and the data reports were generated from GRASP software (proprietary information processing software) used by the center.

“In the simulation model, task priorities, task schedules, and nurse priorities were fed, and simulated nurse agents had to complete those tasks based on the priorities,” Rana reported. “[Ultimately,] nurse workload (tasks in queue: 2, 15, 33) increased with an increase in nurse-to-patient ratio (1:2, 1:4, 1:6). The increase also affected the missed care parameter (17, 24, 53) and task in queue time (0.3, 1, 1.2 hours).

Another literature takeaway stemmed from Melman, et al. (2021), studying how different hospital resources are distributed between COVID-19 and non-COVID-19 patients in Addenbrooke’s hospital, UK. Rana reports that the authors developed a DES model in Arena software, v16, to implement patient flows and evaluate the impact of adopted resource allocation strategies on both groups.

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“Authors found that covid-19 patients are more likely to need critical care, have a more extended stay, and have a significantly higher probability of dying in critical care than non-covid surgical patients,” he writes.

Next, the literature review assesses Kuncová, et al. (2021), and the simulated drive-in COVI-19 sample collection operation in one of Prague’s hospitals. According to Rana, patients arriving by car used drive-in collection points; therefore, the aim of the study was to find the doctors and medics required to reduce the waiting time in queue, and the length of the queue should not exceed 30.

“The authors collected data from the hospital and interviewed a few doctors and patients, and, based upon that, developed the DES model on SIMUL8,” Rana writes. “It was found the queue time with only one doctor, and no medic was 83.53 minutes, whereas adding one medic had a huge impact on queuing time, reducing it to 5.78 minutes.”

Other pieces of literature discussed include:

  • (Hage, et al., 2021) developed a discrete event simulation model in SIMIO package to represent the Covid-19 testing process in the University of Maryland testing center. The model focused on testing operations (such as barcode scanning, uncapping, de-swabbing, etc.) being carried out in the laboratory after the arrival of collected samples.
  • Bartenschlager, et al. (2022) analyzed admission processes of a German maximum care university hospital, the University Hospital of Augsburg, and built a simulation model in Analogic to replicate the process. The staff arrival and patient arrival flow involved only one step in their process, whereas visitor admission involved several queues, starting with waiting in an initial sitting space.
  • Zemaitis, et al. (2021) used discrete event simulation modeling for an outpatient laboratory clinic, the University of Michigan Canton Health Center, to maintain covid-19 rules and guidelines in the laboratory. Many services were provided in the clinic (diagnostic, curative, consultative). The study’s primary goal was to analyze the blood draw process and reduce the number of patients in the waiting area and the average and maximum queuing time.
  • Windeler, et al. (2021) and Powertrain Operations Manufacturing Engineering (PTME) team built a simulation model to facilitate the conversion of their Ford Motor Dagenham plant into a ventilator production facility. The team used Lanner’s Witness Horizon simulation software for the simulation purpose. The model was divided into two main areas: Assembly of ventilators, and testing, with five and three subcategories in respective areas.

Healthcare Simulation Literature Review Takeaways

In conclusion, Rana shared that changing any hospital or clinical process for experimental purposes would disturb the whole system and might lead to a fatal injury or death. Based on this conclusion, he suggests that discrete event modeling alternatively provides a feasible option to modify and optimize the existing processes.

Further, he says that DES helps identify the utilization rate of different operators involved in the healthcare domain, such as nurses, physicians, medical assistants, and lab technicians. While COVID-19 was an unpredictable time, with an exponential increase in patients and a shortage in hospital ICU beds, ventilators, oxygen cylinders, PPE kits, and medical staff, Rana believes the pandemic has been a catalyst that increased healthcare simulation modeling.

Coronavirus COVID-19 Medical Simulation Resources List

“DES allows the evaluation of different ‘what if’ scenarios that could arise during brainstorming of new process flows and helps in making data-driven decisions,” Rana writes. “The existing hospital and clinical processes can be altered based on past data and stakeholders, as seen in most research papers in this study; nevertheless, new processes can also be initiated with the help of discrete event simulation.”

Note: According to Rana, all research papers included in this study were related to discrete event simulation in healthcare during COVID-19, except two. The research was done by Qureshi, et al. (2019) on the nurse-patient ratio that happened before the pandemic; however, it could also be related to the pandemic because of the shortage of nurses in hospitals and increased workload. Another research was conducted by Windeler, et al. (2021) and the team, which was based on converting an existing Ford Motor production line into an emergency ventilator manufacturing line. A shortage of ventilators was forecasted by research done at Imperial College London, for which a Ventilator Challenge U.K. consortium was formed.

Read the Full Literature Review Article Here

Bartenschlager, C. C. et al., 2022. Managing hospital visitor admission during Covid-19: A discrete-event simulation by the data of a German University Hospital. s.l., Proceedings of the 55th Hawaii International Conference on System Sciences.

Hage, J. E. et al., 2021. Supporting scale-up of COVID-19 RT-PCR testing processes with discrete event simulation. PLoS ONE, 16(7), p. E0255214.

Kuncová, M., Svitková, K., Vacková, A. & Vaňková, M., 2021. Discrete event simulation of the covid-19 sample collection point operation. ECMS, pp. 102–108.

Melman, G., Parlikad, A. & Cameron, E., 2021. Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation. Health Care Management Science, 24(2), pp. 356–374.

Qureshi, S. M., Purdy, N., Mohani, A. & Neumann, W. P., 2019. Predicting the effect of nurse–patient ratio on nurse workload and care quality using discrete event simulation. Journal of nursing management, 27(5), pp. 971–980.

Saidani, M., Kim, H. & Kim, J., 2021. Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus. PLoS ONE, 16(6), p. E0253869.

Windeler, M., Higgins, M. & Thomas, G., 2021. Supporting the ventilator challenge during the covid-19 pandemic with discrete event simulation modelling. s.l., Operational Research Society Simulation Workshop 2021.

Zemaitis, D. et al., 2021. Using discrete-event simulation to address COVID-19 health and safety guidelines in outpatient laboratory clinic.

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