Institute of Anaesthesiology, University of Zurich and University Hospital Zurich, Zurich, Switzerland.
Department of Epidemiology and Biostatistics, University of Zurich, Zurich, Switzerland.
Sci Rep. 2023 Apr 11;13(1):5908. doi: 10.1038/s41598-023-33027-z.
Patient monitoring is the foundation of intensive care medicine. High workload and information overload can impair situation awareness of staff, thus leading to loss of important information about patients' 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 signs and patient installation data. It incorporates user-centred design principles to foster situation awareness. This study investigated the avatar's effects on information transfer measured by performance, diagnostic confidence and perceived workload. This computer-based study compared Visual-Patient-avatar ICU and conventional monitor modality for the first time. We recruited 25 nurses and 25 physicians from five centres. The participants completed an equal number of scenarios in both modalities. Information transfer, as the primary outcome, was defined as correctly assessing vital signs and installations. Secondary outcomes included diagnostic confidence and perceived workload. For analysis, we used mixed models and matched odds ratios. 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 to - 6.07; P < 0.001) than conventional modality. Using Visual-Patient-avatar ICU, participants retrieved more information with higher diagnostic confidence and lower perceived workload compared to the current industry standard monitor.
患者监测是重症监护医学的基础。高工作量和信息过载会损害工作人员的态势感知能力，从而导致患者病情的重要信息丢失。为了方便对患者监测数据进行心理处理，我们开发了可视化患者化身重症监护病房（ICU），这是一种从生命体征和患者安装数据中动画化的虚拟患者模型。它结合了以用户为中心的设计原则，以培养态势感知能力。这项研究调查了化身对通过绩效、诊断信心和感知工作量衡量的信息传递的影响。这项基于计算机的研究首次比较了可视化患者化身 ICU 和传统监测模式。我们从五个中心招募了 25 名护士和 25 名医生。参与者在两种模式下完成了相同数量的场景。信息传递是主要结果，定义为正确评估生命体征和安装。次要结果包括诊断信心和感知工作量。对于分析，我们使用混合模型和匹配的优势比。比较 250 个个体案例表明，可视化患者化身 ICU 导致更高的正确评估生命体征和安装的比例[比率比（RR）1.25；95%置信区间（CI）1.19-1.31；P<0.001]，增强了诊断信心[优势比（OR）3.32；95%置信区间（CI）2.15-5.11，P<0.001]，降低了感知工作量（系数-7.62；95%置信区间（CI）-9.17 至-6.07；P<0.001），与传统模式相比。与当前的行业标准监测相比，使用可视化患者化身 ICU，参与者检索到更多信息，具有更高的诊断信心和更低的感知工作量。
