Avatar-based patient monitoring for intensive care units improves information transfer, diagnostic confidence and decreases perceived workload- a computer- based, multicentre comparison study

Abstract Background Patient monitoring is the foundation of intensive care. High workload and information overload can impair situation awareness of staff, thus leading to loss of important information about patient's conditions. To facilitate mental processing of patient monitoring data, we developed the Visual-Patient-avatar Intensive Care Unit (ICU), a virtual patient model animated from vital sign and patient installation data. It incorporates user-centered design principles to foster situation awareness. This study investigated the avatar's effects on information transfer measured by performance, diagnostic confidence and perceived workload. Methods This study compared Visual-Patient-avatar ICU and conventional monitor modality. We recruited 25 nurses and 25 physicians from five centers. The participants completed an equal number of scenarios in both modalities. Information transfer, as the primary outcome was defined as correctly assessed vital signs and installations. Secondary outcomes included diagnostic confidence and perceived workload. For analysis, we used mixed models and matched odds ratios. Results Comparing 250 within-subject cases revealed that Visual-Patient-avatar ICU led to a higher rate of correctly assessed vital signs and installations (rate ratio (RR), 1.25; 95% CI, 1.19–1.31; P < 0.001), strengthened diagnostic confidence (odds ratio (OR), 3.32; 95% CI, 2.15–5.11, P < 0.001) and lowered perceived workload (Coefficient, − 7.62; 95% CI, − 9.17- −6.07; P < 0.001) than conventional modality. Conclusion Using Visual-Patient-avatar ICU, participants retrieved more information with higher diagnostic confidence and lower perceived workload compared to the current industry standard.