Evidence map›Paper›PMID 31317870›Full record

ArticleJournal of medical Internet research2019

Avatar-Based Patient Monitoring With Peripheral Vision: A Multicenter Comparative Eye-Tracking Study.

Juliane Pfarr, Michael T Ganter, Donat R Spahn, Christoph B Noethiger, David W Tscholl

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
4.6field-weighted citation impact, top 5% 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

18 citing papers in PubMed, 33 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

5 authors at 2 institutions in 1 country.

Juliane Pfarr *Institute of Anesthesiology, University and University Hospital Zurich, Zurich, Switzerland.ORCID 0000-0002-0713-7734
Michael T GanterInstitute of Anesthesiology Kantonsspital Winterthur, Winterthur, Switzerland.ORCID 0000-0003-1234-1833
Donat R SpahnInstitute of Anesthesiology, University and University Hospital Zurich, Zurich, Switzerland.ORCID 0000-0002-4782-183X
Christoph B NoethigerInstitute of Anesthesiology, University and University Hospital Zurich, Zurich, Switzerland.ORCID 0000-0002-0336-1421
David W Tscholl *Institute of Anesthesiology, University and University Hospital Zurich, Zurich, Switzerland.ORCID 0000-0002-3677-3705
University Hospital of Zurich · CHKantonsspital Winterthur · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundContinuous patient monitoring has been described by the World Health Organization as extremely important and is widely used in anesthesia, intensive care medicine, and emergency medicine. However, current state-of-the-art number- and waveform-based monitoring does not ideally support human users in acquiring quick, confident interpretations with low cognitive effort, and there are additional problematic aspects such as alarm fatigue. We developed a visualization technology (Visual Patient), specifically designed to help caregivers gain situation awareness quickly, which presents vital sign information in the form of an animated avatar of the monitored patient. We suspected that because of the way it displays the information as large, colorful, moving graphic objects, caregivers might be able to perform patient monitoring using their peripheral vision, which may facilitate quicker detection of anomalies, independently of acoustic alarms.

objectiveIn this study, we tested the hypothesis that avatar-based monitoring, when observed with peripheral vision only, increases the number of perceptible changes in patient status as well as caregivers' perceived diagnostic confidence compared with a high-fidelity simulation of conventional monitoring, when observed with peripheral vision only.

methodsWe conducted a multicenter comparative study with a within-participant design in which anesthesiologists with their peripheral field of vision looked at 2 patient-monitoring scenarios and tried to identify changes in patient status. To ensure the best possible experimental conditions, we used an eye tracker, which recorded the eye movements of the participants and confirmed that they only looked at the monitoring scenarios with their peripheral vision.

resultsOverall, 30 participants evaluated 18 different patient status changes with each technology (avatar and conventional patient monitoring). With conventional patient monitoring, participants could only detect those 3 changes in patient status that are associated with a change in the auditory pulse tone display, that is, tachycardia (faster beeping), bradycardia (slower beeping), and desaturation (lower pitch of beeping). With the avatar, the median number of detected vital sign changes quadrupled from 3 to 12 (P<.001) in scenario 1, and more than doubled from 3 to 8 (P<.001) in scenario 2. Median perceived diagnostic confidence was confident for both scenarios with the avatar and unconfident in scenario 1 (P<.001), and very unconfident in scenario 2 (P=.024) with conventional monitoring.

conclusionsThis study introduces the concept of peripheral vision monitoring. The test performed showed clearly that an avatar-based display is superior to a standard numeric display for peripheral vision. Avatar-based monitoring could potentially make much more of the patient monitoring information available to caregivers for longer time periods per case. Our results indicate that the optimal information transmission would consist of a combination of auditory and avatar-based monitoring.

Indexed as

AdultEye MovementsFemaleHumansMaleMiddle AgedMonitoring, Physiologicanesthesiacomputerscritical carediagnosispatient monitoringperceptionsituation awarenessvision

Identifiers

PMID31317870
PMCPMC6668297
OpenAlexW2949783711

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.