Literature DB >> 32205304

Comparing Classroom Instruction to Individual Instruction as an Approach to Teach Avatar-Based Patient Monitoring With Visual Patient: Simulation Study.

Julian Rössler1, Alexander Kaserer1, Benjamin Albiez1, Julia Braun2, Jan Breckwoldt1, Donat Rudolf Spahn1, Christoph Nöthiger1, David Werner Tscholl1.   

Abstract

BACKGROUND: Visual Patient is an avatar-based alternative to standard patient monitor displays that significantly improves the perception of vital signs. Implementation of this technology in larger organizations would require it to be teachable by brief class instruction to large groups of professionals. Therefore, our study aimed to investigate the efficacy of such a large-scale introduction to Visual Patient.
OBJECTIVE: In this study, we aimed to compare 2 different educational methods, one-on-one instruction and class instruction, for training anesthesia providers in avatar-based patient monitoring.
METHODS: We presented 42 anesthesia providers with 30 minutes of class instruction on Visual Patient (class instruction group). We further selected a historical sample of 16 participants from a previous study who each received individual instruction (individual instruction group). After the instruction, the participants were shown monitors with either conventional displays or Visual Patient displays and were asked to interpret vital signs. In the class instruction group, the participants were shown scenarios for either 3 or 10 seconds, and the numbers of correct perceptions with each technology were compared. Then, the teaching efficacy of the class instruction was compared with that of the individual instruction in the historical sample by 2-way mixed analysis of variance and mixed regression.
RESULTS: In the class instruction group, when participants were presented with the 3-second scenario, there was a statistically significant median increase in the number of perceived vital signs when the participants were shown the Visual Patient compared to when they were shown the conventional display (3 vital signs, P<.001; effect size -0.55). No significant difference was found for the 10-second scenarios. There was a statistically significant interaction between the teaching intervention and display technology in the number of perceived vital signs (P=.04; partial η2=.076). The mixed logistic regression model for correct vital sign perception yielded an odds ratio (OR) of 1.88 (95% CI 1.41-2.52; P<.001) for individual instruction compared to class instruction as well as an OR of 3.03 (95% CI 2.50-3.70; P<.001) for the Visual Patient compared to conventional monitoring.
CONCLUSIONS: Although individual instruction on Visual Patient is slightly more effective, class instruction is a viable teaching method; thus, large-scale introduction of health care providers to this novel technology is feasible. ©Julian Werner Rössler, Alexander Kaserer, Benjamin Albiez, Julia Braun, Jan Breckwoldt, Donat Rudolf Spahn, Christoph Nöthiger, David Werner Tscholl. Originally published in JMIR Medical Education (http://mededu.jmir.org), 23.04.2020.

Entities:  

Keywords:  avatar; computer-assisted; diagnosis

Year:  2020        PMID: 32205304     DOI: 10.2196/17922

Source DB:  PubMed          Journal:  JMIR Med Educ        ISSN: 2369-3762


  3 in total

1.  Improving Visual-Patient-Avatar Design Prior to Its Clinical Release: A Mixed Qualitative and Quantitative Study.

Authors:  Doreen J Wetli; Lisa Bergauer; Christoph B Nöthiger; Tadzio R Roche; Donat R Spahn; David W Tscholl; Sadiq Said
Journal:  Diagnostics (Basel)       Date:  2022-02-21

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

Authors:  Arsène Ljubenovic; Sadiq Said; Julia Braun; Bastian Grande; Michaela Kolbe; Donat R Spahn; Christoph B Nöthiger; David W Tscholl; Tadzio R Roche
Journal:  JMIR Serious Games       Date:  2022-03-22       Impact factor: 3.364

Review 3.  Situation Awareness-Oriented Patient Monitoring with Visual Patient Technology: A Qualitative Review of the Primary Research.

Authors:  David Werner Tscholl; Julian Rössler; Sadiq Said; Alexander Kaserer; Donat Rudolf Spahn; Christoph Beat Nöthiger
Journal:  Sensors (Basel)       Date:  2020-04-09       Impact factor: 3.576

  3 in total

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