Literature DB >> 32910105

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

Reinis Balmaks1, Alise Grāmatniece, Aija Vilde, Mārtiņš Ļuļļa, Uga Dumpis, Isabel Theresia Gross, Ieva Šlēziņa.   

Abstract

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19)] outbreak has been declared a global pandemic by the World Health Organization. The COVID-19 pandemic has highlighted problems of sustainable infection prevention and control measures worldwide, particularly the emerging issues with an insufficient supply of personal protective equipment. The aim of this study was to provide an action plan for mitigation of occupational hazards and nosocomial spread of SARS-CoV-2 through a failure mode analysis based on observations during in situ simulations.
METHODS: A multicenter, cross-sectional, observational, simulation-based study was performed in Latvia from March 2 to 26, 2020. This study was conducted at 7 hospitals affiliated with Riga Stradiņš University. The presentation of a COVID-19 patient was simulated with an in situ simulations, followed by a structured debrief. Healthcare Failure Modes and Effects Analysis is a tool for conducting a systematic, proactive analysis of a process in which harm may occur. We used Healthcare Failure Modes and Effects Analysis to analyze performance gaps and systemic issues.
RESULTS: A total of 67 healthcare workers from 7 hospitals participated in the study (range = 4-17). A total of 32 observed failure modes were rated using a risk matrix. Twenty-seven failure modes (84.4%) were classified as either medium or high risk or were single-point weaknesses, hence evaluated for action type and action; 11 (40.7%) were related to organizational, 11 (40.7%) to individual, and 5 (18.5%) to environmental factors.
CONCLUSIONS: Simulation-based failure mode analysis helped us identify the risks related to the preparedness of the healthcare workers and emergency departments for the COVID-19 pandemic in Latvia. We believe that this approach can be implemented to assess and maintain readiness for the outbreaks of emerging infectious diseases in the future.
Copyright © 2020 Society for Simulation in Healthcare.

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Mesh:

Year:  2021        PMID: 32910105     DOI: 10.1097/SIH.0000000000000506

Source DB:  PubMed          Journal:  Simul Healthc        ISSN: 1559-2332            Impact factor:   1.929


  1 in total

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

Authors:  Michael Saidani; Harrison Kim; Jinju Kim
Journal:  PLoS One       Date:  2021-06-29       Impact factor: 3.240

  1 in total

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