可视化
工作量
数据科学
信息可视化
医学
领域(数学)
阿凡达
计算机科学
人机交互
数据挖掘
数学
操作系统
纯数学
作者
Greta Gasciauskaite,Justyna Lunkiewicz,Tadzio R. Roche,Donat R. Spahn,Christoph B. Nöthiger,David W. Tscholl
出处
期刊:Critical Care
[Springer Nature]
日期:2023-06-28
卷期号:27 (1)
被引量:10
标识
DOI:10.1186/s13054-023-04544-0
摘要
Abstract Medical technology innovation has improved patient monitoring in perioperative and intensive care medicine and continuous improvement in the technology is now a central focus in this field. Because data density increases with the number of parameters captured by patient-monitoring devices, its interpretation has become more challenging. Therefore, it is necessary to support clinicians in managing information overload while improving their awareness and understanding about the patient’s health status. Patient monitoring has almost exclusively operated on the single-sensor–single-indicator principle—a technology-centered way of presenting data in which specific parameters are measured and displayed individually as separate numbers and waves. An alternative is user-centered medical visualization technology, which integrates multiple pieces of information (e.g., vital signs), derived from multiple sensors into a single indicator—an avatar-based visualization—that is a meaningful representation of the real-world situation. Data are presented as changing shapes, colors, and animation frequencies, which can be perceived, integrated, and interpreted much more efficiently than other formats (e.g., numbers). The beneficial effects of these technologies have been confirmed in computer-based simulation studies; visualization technologies improved clinicians’ situation awareness by helping them effectively perceive and verbalize the underlying medical issue, while improving diagnostic confidence and reducing workload. This review presents an overview of the scientific results and the evidence for the validity of these technologies.
科研通智能强力驱动
Strongly Powered by AbleSci AI