Consider the problem of reducing the time needed by healthcare professionals to understand patient medical history via the next generation of biomedical decision support. This problem is societally important because it has the potential to improve healthcare quality and patient outcomes. However, it is challenging due to the high patient-doctor ratio, the potential long medical histories, the urgency of treatment for some medical conditions, and patient variability. The current system provides a longitudinal view of patient medical history, which is time-consuming to browse, and doctors often need to engage nurses, residents, and others for initial analysis. To overcome this limitation, our vision, Atlas EHR, is an alternative spatial representation of patients' histories (e.g., electronic health records (EHRs)) and other biomedical data. Just like Google Maps allows a global, national, regional, and local view, the Atlas-EHR may start with the overview of the patient's anatomy and history before drilling down to spatially anatomical sub-systems, their individual components, or sub-components. It will also use thoughtful cartography (e.g., urgency color, disease icons, and symbols) to highlight critical information for improving task efficiency and decision quality, analogous to how it is used in designing task-specific maps. Atlas-EHR presents a compelling opportunity for spatial computing since health is almost a fifth of the US economy. However, the traditional spatial computing designed for geographic use cases (e.g., navigation, land survey, mapping) faces many hurdles in the biomedical domain, presenting several research questions. This paper presents some open research questions under this theme in broad areas of spatial computing.

翻译：考虑通过下一代生物医学决策支持来减少医疗专业人员理解患者病史所需时间的问题。这一问题具有重要的社会意义，因为它有潜力提高医疗质量和患者预后。然而，由于患者与医生的高比例、潜在的长病史、某些医疗状况的紧急治疗需求以及患者个体差异，这一任务充满挑战。现有系统提供患者病史的纵向视图，浏览起来耗时，且医生通常需要护士、住院医师及其他人员进行初步分析。为克服这一局限，我们提出的愿景——Atlas EHR——是一种替代性的空间表示方法，用于呈现患者病史（如电子健康记录）及其他生物医学数据。正如谷歌地图允许从全球、国家、区域到地方的多层级视图，Atlas-EHR可以从患者解剖结构和病史的概览开始，逐步深入到空间解剖子系统、其独立组件或子组件。它还将采用精心设计的制图技术（如紧急程度颜色、疾病图标和符号）来突出关键信息，以提高任务效率和决策质量——类似于设计任务特定地图时的做法。Atlas-EHR为空间计算提供了引人注目的机遇，因为医疗健康几乎占美国经济的五分之一。然而，传统的专为地理用途（如导航、土地测量、地图制作）设计的空间计算在生物医学领域面临诸多障碍，提出了若干研究问题。本文在此主题下，提出了空间计算广泛领域中的一些开放性研究问题。