Evidence map›Paper›PMID 33052819›Full record

ArticleThe western journal of emergency medicine2020

In-situ Simulation Use for Rapid Implementation and Process Improvement of COVID-19 Airway Management.

Brendan W Munzer, Benjamin S Bassin, William J Peterson, Ryan V Tucker, Jessica Doan, Carrie Harvey, Nana Sefa, Cindy H Hsu

Abstract read
In one paragraph

Article in The western journal of emergency medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. A Newly Developed InterprofessionalJournal of clinical medicine research · 2022
    Article
  8. Article
  9. Article
  10. Rapid Dissemination of a COVID-19 Airway Management Simulation Using a Train-the-Trainers Curriculum.Academic medicine : journal of the Association of American Medical Colleges · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Brendan W MunzerMichigan Medicine, Department of Emergency Medicine, Ann Arbor, Michigan.
Benjamin S BassinMichigan Medicine, Department of Emergency Medicine, Ann Arbor, Michigan.
William J PetersonMichigan Medicine, Department of Emergency Medicine, Ann Arbor, Michigan.
Ryan V TuckerMichigan Medicine, Department of Emergency Medicine, Ann Arbor, Michigan.
Jessica DoanMichigan Medicine, Department of Emergency Medicine, Ann Arbor, Michigan.
Carrie HarveyMichigan Medicine, Department of Emergency Medicine, Ann Arbor, Michigan.
Nana SefaMichigan Medicine, Department of Emergency Medicine, Ann Arbor, Michigan.
Cindy H HsuMichigan Medicine, Department of Emergency Medicine, Ann Arbor, Michigan.

Funding

K12: Career Development in Emergency Critical Care ResearchK12HL133304 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NEUMAR, ROBERT W., PINSKY, DAVID J. · 2016 to 2020
$2.2M
NHLBI NIH HHS K12 HL133304
6 · The paper itself

Abstract

introductionThe coronavirus disease 2019 (COVID-19) pandemic presents unique challenges to frontline healthcare workers. In order to safely care for patients new processes, such as a plan for the airway management of a patient with COVID-19, must be implemented and disseminated in a rapid fashion. The use of in-situ simulation has been used to assist in latent problem identification as part of a Plan-Do-Study-Act cycle. Additionally, simulation is an effective means for training teams to perform high-risk procedures before engaging in the actual procedure. This educational advance seeks to use and study in-situ simulation as a means to rapidly implement a process for airway management in patients with COVID-19.

methodsUsing an airway algorithm developed by the authors, we designed an in-situ simulation scenario to train physicians, nurses, and respiratory therapists in best practices for airway management of patients with COVID-19. Physician participants were surveyed using a five-point Likert scale with regard to their comfort level with various aspects of the airway algorithm both before and after the simulation in a retrospective fashion. Additionally, we obtained feedback from all participants and used it to refine the airway algorithm.

resultsOver a two-week period, 93 physicians participated in the simulation. We received 81 responses to the survey (87%), which showed that the average level of comfort with personal protective equipment procedures increased significantly from 2.94 (95% confidence interval, 2.71-3.17) to 4.36 (4.24-4.48), a difference of 1.42 (1.20-1.63, p < 0.001). There was a significant increase in average comfort level in understanding the physician role with scores increasing from 3.51 (3.26-3.77) to 4.55 (2.71-3.17), a difference of 1.04 (0.82-1.25, p < 0.001). There was also increased comfort in performing procedural tasks such as intubation, from 3.08 (2.80-3.35) to 4.38 (4.23-4.52) after the simulation, a difference of 1.30 points (1.06-1.54, p < 0.001). Feedback from the participants also led to refinement of the airway algorithm.

conclusionWe successfully implemented a new airway management guideline for patients with suspected COVID-19. In-situ simulation is an essential tool for both dissemination and onboarding, as well as process improvement, in the context of an epidemic or pandemic.

Indexed as

Simulation TrainingAirway ManagementAlgorithmsBetacoronavirusCoronavirus InfectionsCOVID-19Emergency Service, HospitalHealth PersonnelHumansMichiganPandemicsPersonal Protective EquipmentPneumonia, ViralPractice Guidelines as TopicSARS-CoV-2Surveys and Questionnaires

Identifiers

PMID33052819
PMCPMC7673893

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.