A Simulation-Based Failure Mode Analysis of SARS-CoV-2 Infection Control and Prevention in Emergency Departments

Balmaks, Reinis; Grāmatniece, Alise; Vilde, Aija; Lulla, Martins; Dumpis, Uga; Gross, Isabel T; Šlēziņa, Ieva

doi:10.1097/sih.0000000000000506

Cited by 3 publications

(5 citation statements)

References 26 publications

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“…scenarios to be tested in an efficient way for decision-making [20]. For instance, simulation-based failure mode analysis can be useful to identify the risks related to the readiness of the healthcare workers and emergency departments for the COVID-19 [21].…”

Section: Plos Onementioning

confidence: 99%

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

Saidani

Kim

2021

PLoS ONE

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Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.

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Section: Plos Onementioning

confidence: 99%

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

Saidani

Kim

2021

PLoS ONE

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“…66.6% (n = 4 articles) of the total related body of knowledge utilized a single technique for dealing with different ED performance challenges during the current pandemic. An interesting QM-based work is reported in Balmaks et al [48] who analyzed the performance gaps and failures generated in their system through a multicenter and crosssectional study based on the simulation of COVID-19 demands and their effects upon the normal ED operation. On a different tack, Teklewold et al [94] implemented the failure mode and effect analysis (FMEA) tool to improve the pandemic management in EDs.…”

Section: Techniques From the Quality Management Domainmentioning

confidence: 99%

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

Ortíz‐Barrios

Coba-Blanco

Alfaro-Saiz

et al. 2021

IJERPH

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The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

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“…Finally, we used a taxonomy of system failure mode for system-focused simulation, following our established hospital-level clinical safety event reporting system. There are other ways to categorize failure modes, including human factors approaches and latent safety threats 3,7,19,20 ; as a result, our findings may be restricted by the applied taxonomy.…”

Section: Limitationsmentioning

confidence: 99%

“…Simulation has been used as a powerful tool to train clinicians and as a method to explore new approaches to safely care for patients with suspected or confirmed COVID-19 infection. [4][5][6][7][8] Historically, simulation literature has focused on specific skills or device development, but there is an emerging body of work describing simulation approaches to address system-level gaps, particularly in the emergency and disaster response settings. 7,[9][10][11][12] A survey-based study has described adaptations by simulation centers worldwide, 13 but a comprehensive quantitative description of how hospital-based simulation centers can support the system during a pandemic is lacking.…”

mentioning

confidence: 99%

“…At the same time, infection control measures imposed by the pandemic required a temporary curtailment of in-person educational activities. Simulation has been used as a powerful tool to train clinicians and as a method to explore new approaches to safely care for patients with suspected or confirmed COVID-19 infection 4–8 . Historically, simulation literature has focused on specific skills or device development, but there is an emerging body of work describing simulation approaches to address system-level gaps, particularly in the emergency and disaster response settings 7,9–12 .…”

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confidence: 99%

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et al. 2021

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IntroductionThe COVID-19 pandemic forced healthcare institutions to rapidly adapt practices for patient care, staff safety, and resource management. We evaluated contributions of the simulation center in a freestanding children's hospital during the early stages of the pandemic.MethodsWe reviewed our simulation center's activity for education-based and system-focused simulation for 2 consecutive academic years (AY19: 2018–2019 and AY20: 2019–2020). We used statistical control charts and χ2 analyses to assess the impact of the pandemic on simulation activity as well as outputs of system-focused simulation during the first wave of the pandemic (March–June 2020) using the system failure mode taxonomy and required level of resolution.ResultsA total of 1983 event counts were reported. Total counts were similar between years (994 in AY19 and 989 in AY20). System-focused simulation was more prevalent in AY20 compared with AY19 (8% vs. 2% of total simulation activity, P < 0.001), mainly driven by COVID-19–related simulation events. COVID-19–related simulation occurred across the institution, identified system failure modes in all categories except culture, and was more likely to identify macro-level issues than non–COVID-19–related simulation (64% vs. 44%, P = 0.027).ConclusionsOur simulation center pivoted to deliver substantial system-focused simulation across the hospital during the first wave of the COVID-19 pandemic. Our experience suggests that simulation centers are essential resources in achieving safe and effective hospital-wide improvement.

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A Simulation-Based Failure Mode Analysis of SARS-CoV-2 Infection Control and Prevention in Emergency Departments

Cited by 3 publications

References 26 publications

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

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