Evidence map›Paper›PMID 35172958›Full record

ArticleJMIR serious games2022

Visual Attention of Anesthesia Providers in Simulated Anesthesia Emergencies Using Conventional Number-Based and Avatar-Based Patient Monitoring: Prospective Eye-Tracking Study.

Arsène Ljubenovic, Sadiq Said, Julia Braun, Bastian Grande, Michaela Kolbe, Donat R Spahn, Christoph B Nöthiger, David W Tscholl, Tadzio R Roche

Abstract read
In one paragraph

Article in JMIR serious games, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

9 authors.

Arsène LjubenovicInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-7535-5365
Sadiq SaidInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-2037-4766
Julia BraunDepartments of Epidemiology and Biostatistics, Institute of Epidemiology, Biostatistics and Prevention, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-8359-6177
Bastian GrandeInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-2935-1317
Michaela KolbeSimulation Centre, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-6654-6370
Donat R SpahnInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-4782-183X
Christoph B NöthigerInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-0336-1421
David W TschollInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-3677-3705
Tadzio R RocheInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-1072-5748

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInadequate situational awareness accounts for two-thirds of preventable complications in anesthesia. An essential tool for situational awareness in the perioperative setting is the patient monitor. However, the conventional monitor has several weaknesses. Avatar-based patient monitoring may address these shortcomings and promote situation awareness, a prerequisite for good decision making.

objectiveThe spatial distribution of visual attention is a fundamental process for achieving adequate situation awareness and thus a potential quantifiable surrogate for situation awareness. Moreover, measuring visual attention with a head-mounted eye-tracker may provide insights into usage and acceptance of the new avatar-based patient monitoring modality.

methodsThis prospective eye-tracking study compared anesthesia providers' visual attention on conventional and avatar-based patient monitors during simulated critical anesthesia events. We defined visual attention, measured as fixation count and dwell time, as our primary outcome. We correlated visual attention with the potential confounders: performance in managing simulated critical anesthesia events (task performance), work experience, and profession. We used mixed linear models to analyze the results.

resultsFifty-two teams performed 156 simulations. After a manual quality check of the eye-tracking footage, we excluded 57 simulations due to technical problems and quality issues. Participants had a median of 198 (IQR 92.5-317.5) fixations on the patient monitor with a median dwell time of 30.2 (IQR 14.9-51.3) seconds. We found no significant difference in participants' visual attention when using avatar-based patient monitoring or conventional patient monitoring. However, we found that with each percentage point of better task performance, the number of fixations decreased by about 1.39 (coefficient -1.39; 95% CI -2.44 to -0.34; P=.02), and the dwell time diminished by 0.23 seconds (coefficient -0.23; 95% CI: -0.4 to -0.06; P=.01).

conclusionsUsing eye tracking, we found no significant difference in visual attention when anesthesia providers used avatar-based monitoring or conventional patient monitoring in simulated critical anesthesia events. However, we identified visual attention in conjunction with task performance as a surrogate for situational awareness.

Indexed as

Anesthesiaavatar based modeleye-tracking technologypatient monitoringpatient simulationperioperativesimulated anesthesiasituation awarenesstask performancevisual attention

Identifiers

PMID35172958
PMCPMC8984829

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Registered trials

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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.