Evidence map›Paper›PMID 35637450›Full record

ArticleBMC anesthesiology2022

Anesthesia personnel's visual attention regarding patient monitoring in simulated non-critical and critical situations, an eye-tracking study.

Tadzio R Roche, Elise J C Maas, Sadiq Said, Julia Braun, Carl Machado, Donat R Spahn, Christoph B Noethiger, David W Tscholl

Open access · goldAbstract read
In one paragraph

Article in BMC anesthesiology, 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
1.3field-weighted citation impact, top 21% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

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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

8 authors at 3 institutions in 2 countries.

Tadzio R RocheInstitute of Anesthesiology, University of Zurich and University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland. tadzioraoul.roche@usz.ch.
Elise J C MaasAthena Institute, Faculty of Science, VU University of Amsterdam, De Boelelaan 1085, 1091, HV, Amsterdam, The Netherlands.
Sadiq SaidInstitute of Anesthesiology, University of Zurich and University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
Julia BraunDepartments of Epidemiology and Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, 8001, Zurich, Switzerland.
Carl MachadoPhilips Research Eindhoven, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands.
Donat R SpahnInstitute of Anesthesiology, University of Zurich and University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
Christoph B NoethigerInstitute of Anesthesiology, University of Zurich and University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
David W TschollInstitute of Anesthesiology, University of Zurich and University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
University of Zurich · CHPhilips (Netherlands) · NLUniversity Hospital of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCognitive ergonomics design of patient monitoring may reduce human factor errors in high-stress environments. Eye-tracking is a suitable tool to gain insight into the distribution of visual attention of healthcare professionals with patient monitors, which may facilitate their further development.

methodsThis prospective, exploratory, high-fidelity simulation study compared anesthesia personnel's visual attention (fixation count and dwell-time) to 15 areas of interest on the patient monitor during non-critical and critical anesthesia situations. Furthermore, we examined the extent to which participants' experience influenced visual attention and which vital signs displayed on the patient monitor received the most visual attention. We used mixed zero-inflated Poisson regression and mixed linear models to analyze the data.

resultsAnalyzing 23 ten-minute scenarios, we found significantly more fixations to the areas of interest on the patient monitor during critical than non-critical situations (rate ratio of 1.45; 95% CI 1.33 to 1.59; p < 0.001). However, the dwell-time on the areas of interest did not significantly differ between the non-critical and critical situations (coefficient of - 1.667; 95% CI - 4.549 to 1.229; p = 0.27). The professional experience did not significantly influence the visual attention (fixation: rate ratio of 0.88; 95% CI 0.54 to 1.43; p = 0.61 and dwell-time: coefficient of 0.889; 95% CI - 1.465 to 3.229; p = 0.27). Over all situations, anesthesia personnel paid the most attention to the vital signs blood pressure (fixation: mean [SD] of 108 [74.83]; dwell-time: mean [SD] of 27 [15.90] seconds), end-expiratory carbon dioxide (fixation: mean [SD] of 59 [47.39]; dwell-time: mean [SD] of 30 [21.51] seconds), and the electrocardiogram (fixation: mean [SD] of 58 [64.70]; dwell-time: mean [SD] of 15 [14.95] seconds).

conclusionsCritical anesthesia situations increased anesthesia personnel's visual interaction with the patient monitor. Furthermore, we found that their visual attention focused mainly on a few vital signs. To assist clinicians in critical situations, manufacturers should optimize monitors to convey necessary information as easily and quickly as possible and optimize the visibility of less frequently observed but equally critical vital signs, especially when they are in an abnormal range.

Indexed as

AnesthesiaAnesthesiologyEye-Tracking TechnologyHumansMonitoring, PhysiologicProspective StudiesAnesthesia, generalEye-tracking technologyPatient monitoringPatient simulationSituation awarenessVisual attention

Identifiers

PMID35637450
PMCPMC9149329
OpenAlexW4281624042

What OpenQuestion holds

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LicenceCC BY
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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.