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

10.2196/24459 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e24459
Author(s):  
Julian Rössler ◽  
Alexander Kaserer ◽  
Benjamin Albiez ◽  
Julia Braun ◽  
Jan Breckwoldt ◽  
...  
Keyword(s):  
Simulation Study ◽  
Patient Monitoring ◽  
Classroom Instruction ◽  
Patient Simulation ◽  
Individual Instruction

scholarly journals Correction: Comparing Classroom Instruction to Individual Instruction as an Approach to Teach Avatar-Based Patient Monitoring With Visual Patient: Simulation Study (Preprint)

2020 ◽  
Author(s):  
Julian Rössler ◽  
Alexander Kaserer ◽  
Benjamin Albiez ◽  
Julia Braun ◽  
Jan Breckwoldt ◽  
...  
Keyword(s):  
Simulation Study ◽  
Patient Monitoring ◽  
Classroom Instruction ◽  
Patient Simulation ◽  
Individual Instruction

UNSTRUCTURED Corrigendum Corrigendum Corrigendum Corrigendum Corrigendum Corrigendum

Initial assessment of tumor tracking with a gimbaled linac system in clinical circumstances: A patient simulation study

2013 ◽  
Vol 106 (2) ◽  
pp. 236-240 ◽  
Author(s):  
Tom Depuydt ◽  
Kenneth Poels ◽  
Dirk Verellen ◽  
Benedikt Engels ◽  
Christine Collen ◽  
...  
Keyword(s):  
Simulation Study ◽  
Patient Simulation ◽  
Initial Assessment ◽  
Tumor Tracking

scholarly journals Comparing Classroom Instruction to Individual Instruction as an Approach to Teach Avatar-Based Patient Monitoring With Visual Patient: Simulation Study (Preprint)

2020 ◽  
Author(s):  
Julian Rössler ◽  
Alexander Kaserer ◽  
Benjamin Albiez ◽  
Julia Braun ◽  
Jan Breckwoldt ◽  
...  
Keyword(s):  
Large Scale ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Care Providers ◽  
Instruction Group ◽  
Anesthesia Providers ◽  
Individual Instruction ◽  
Significant Difference ◽  
Historical Sample ◽  
Class Instruction

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, <i>P</i>&lt;.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 (<i>P</i>=.04; partial η<sup>2</sup>=.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; <i>P</i>&lt;.001) for individual instruction compared to class instruction as well as an OR of 3.03 (95% CI 2.50-3.70; <i>P</i>&lt;.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.

scholarly journals Multiple Patient Monitoring in High Dependency Units: A Field Study

2019 ◽  
Vol 63 (1) ◽  
pp. 669-669
Author(s):  
Marie-Lys F. A. Deschamps ◽  
Penelope M. Sanderson
Keyword(s):  
Critical Care ◽  
Simulation Study ◽  
Vital Sign ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Well Being ◽  
Alarm Fatigue ◽  
Critical Care Units ◽  
High Dependency ◽  
Australian Hospital

Much of the focus related to alarm fatigue has been directed towards reducing the number of alarms associated with vital sign monitoring. However, recent fieldwork conducted in four high dependency and critical care units of an Australian hospital suggests that the most problematic alarms were often unassociated with vital signs, such as IV pumps and mattress alarms. Many nurses indicated that they like alarms, even when false, because they support awareness of their patients’ well-being. Results of the fieldwork are guiding the design of a simulation study investigating clinical monitoring displays.

PD-0484 INITIAL CLINICAL ASSESSMENT OF A GIMBALED LINAC TUMOR TRACKING SYSTEM IN A PATIENT SIMULATION STUDY

2012 ◽  
Vol 103 ◽  
pp. S194-S195
Author(s):  
T. Depuydt ◽  
K. Poels ◽  
B. Engels ◽  
C. Haverbeke ◽  
T. Gevaert ◽  
...  
Keyword(s):  
Simulation Study ◽  
Clinical Assessment ◽  
Tracking System ◽  
Patient Simulation ◽  
Tumor Tracking

scholarly journals Avatar-based patient monitoring in critical anaesthesia events: a randomised high-fidelity simulation study

2021 ◽  
Author(s):  
Tadzio R. Roche ◽  
Sadiq Said ◽  
Julia Braun ◽  
Elise J.C. Maas ◽  
Carl Machado ◽  
...  
Keyword(s):  
Simulation Study ◽  
Patient Monitoring ◽  
High Fidelity ◽  
High Fidelity Simulation
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