Evidence map›Paper›PMID 35119375›Full record

ArticleJMIR human factors2022

User Perceptions of Different Vital Signs Monitor Modalities During High-Fidelity Simulation: Semiquantitative Analysis.

Samira Akbas, Sadiq Said, Tadzio Raoul Roche, Christoph B Nöthiger, Donat R Spahn, David W Tscholl, Lisa Bergauer

Open access · goldAbstract read
In one paragraph

Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.0field-weighted citation impact, top 15% of its field
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

8 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  3. A study on the influence of situations on personal avatar characteristics.Visual computing for industry, biomedicine, and art · 2024
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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

7 authors at 1 institution in 1 country.

Samira AkbasInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-7692-4718
Sadiq SaidInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-2037-4766
Tadzio Raoul RocheInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-1072-5748
Christoph B NöthigerInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-0336-1421
Donat R SpahnInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-4782-183X
David W TschollInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-3677-3705
Lisa BergauerInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-9615-6357
University of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatient safety during anesthesia is crucially dependent on the monitoring of vital signs. However, the values obtained must also be perceived and correctly classified by the attending care providers. To facilitate these processes, we developed Visual-Patient-avatar, an animated virtual model of the monitored patient, which innovatively presents numerical and waveform data following user-centered design principles. After a high-fidelity simulation study, we analyzed the participants' perceptions of 3 different monitor modalities, including this newly introduced technique.

objectiveThe aim of this study was to collect and evaluate participants' opinions and experiences regarding 3 different monitor modalities, which are Visual-Patient-avatar, Split Screen (avatar and Conventional monitor alongside each other), and Conventional monitor after using them during simulated critical anesthetic events.

methodsThis study was a researcher-initiated, single-center, semiquantitative study. We asked 92 care providers right after finishing 3 simulated emergency scenarios about their positive and negative opinions concerning the different monitor modalities. We processed the field notes obtained and derived the main categories and corresponding subthemes following qualitative research methods.

resultsWe gained a total of 307 statements. Through a context-based analysis, we identified the 3 main categories of "Visual-Patient-avatar," "Split Screen," and "Conventional monitor" and divided them into 11 positive and negative subthemes. We achieved substantial interrater reliability in assigning the statements to 1 of the topics. Most of the statements concerned the design and usability features of the avatar or the Split Screen mode.

conclusionsThis study semiquantitatively reviewed the clinical applicability of the Visual-Patient-avatar technique in a high-fidelity simulation study and revealed the strengths and limitations of the avatar only and Split Screen modality. In addition to valuable suggestions for improving the design, the requirement for training prior to clinical implementation was emphasized. The responses to the Split Screen suggest that this symbiotic modality generates better situation awareness in combination with numerical data and accurate curves. As a subsequent development step, a real-life introduction study is planned, where we will test the avatar in Split Screen mode under actual clinical conditions.

Indexed as

avatarpatient monitoringsemiquantitative researchsimulation studysituation awarenessuser-centered designvisual-patient-avatar

Identifiers

PMID35119375
PMCPMC8976258
OpenAlexW4210302115

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.