. 2020 Apr 9;20(7):2112. doi: 10.3390/s20072112

Table 2.

Summary of the reviewed literature.

Study (Year)	Study Type ¹	Participants ²	Task	Method	Results
Tscholl et al. (2018) [1]	Within-subject, computer-based	Calibration and validation of avatar: 150 Comparative study: 32	Interpreting patient monitoring scenarios with Visual Patient and conventional patient monitoring	Iterative development Delphi process Rating of vital signs Rating of diagnostic certainty NASA Task Load Index	Visual Patient showed high high interrater reliability, improved vital sign perception, increased diagnostic confidence, and lowered perceived workload.
Tscholl et al. (2018) [2]	Qualitative and quantitative study	Interview part: 128 Quantitative part: 36	Providing user feedback about Visual Patient	Qualitative analysis of interviews followed by quantitative rating of statements	Visual Patient provided quick situation overview and was easy to learn
Pfarr et al. (2019) [4]	Within-subject, computer-based, eye tracking	30	Interpreting patient monitoring scenarios with Visual Patient and conventional patient monitoring with peripheral vision	Rating of vital signs Rating of diagnostic certainty Eye-tracking analysis	Visual Patient improved vital sign perception, and increased diagnostic confidence with peripheral vision
Pfarr et al. (2019) [6]	Within-subject, computer-based	38	Interpreting patient monitoring scenarios with Visual Patient and conventional patient monitoring under distraction	Rating of vital signs NASA Task Load Index	Visual Patient improved vital sign perception and reduced workload under distraction
Garot et al. (2020) [5]	Within-subject, computer-based	38	Interpreting multiple-patient monitoring scenarios with Visual Patient and conventional patient monitoring	Rating of vital signs NASA Task Load Index	Visual Patient improved vital sign perception and reduced workload under distraction except in 30 s scenarios
Tscholl et al. (2020) [3]	Within-subject, computer-based, eye-tracking	30	Interpreting patient monitoring scenarios with Visual Patient and conventional patient monitoring	Eye-tracking analysis	Visual Patient enabled parallel perception of vital signs as a result of its visual design
Rössler et al. (2020) [7]	Between-subject, computer-based	42	Interpreting patient monitoring scenarios with Visual Patient and conventional patient monitoring	Rating of vital signs	Class-based and individual instruction both feasible for Visual Patient training

¹ All studies were two-center studies, except Rössler et al. [7], which was a single-center study. ² Participants were anesthesiologists and certified nurse anesthesiologists for all studies except Rössler et al. [7], in which all participants were certified nurse anesthetists.