We 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, navigating electronic health records is challenging due to the high patient-doctor ratios, potentially long medical histories, the urgency of treatment for some medical conditions, and patient variability. The current electronic health record systems provides only 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, we envision an alternative spatial representation of patients' histories (e.g., electronic health records (EHRs)) and other biomedical data in the form of Atlas-EHR. Just like Google Maps allows a global, national, regional, and local view, the Atlas-EHR may start with an overview of the patient's anatomy and history before drilling down to spatially anatomical sub-systems, their individual components, or sub-components. Atlas-EHR presents a compelling opportunity for spatial computing since healthcare is almost a fifth of the US economy. However, the traditional spatial computing designed for geographic use cases (e.g., navigation, land-surveys, mapping) faces many hurdles in the biomedical domain. This paper presents a number of open research questions under this theme in five broad areas of spatial computing.

翻译：我们探讨了通过下一代生物医学决策支持减少医护人员理解患者病史所需时间的问题。该问题具有重要社会意义，因其有望提升医疗服务质量与患者预后。然而，当前电子健康记录的导航面临诸多挑战：居高不下的医患比例、潜在冗长的病史记录、部分病症的治疗紧迫性，以及患者个体差异。现有电子健康记录系统仅提供患者病史的纵向视图，浏览耗时且往往需要护士、住院医师等人先行初步分析。为突破这一局限，我们提出以Atlas-EHR形式构建患者病史（如电子健康记录）及其他生物医学数据的替代性空间表征。正如谷歌地图支持全球、国家、区域及本地视图的层级切换，Atlas-EHR可从患者解剖结构与病史总览切入，逐步深入至空间解剖子系统、其独立组件或子组件层面。由于医疗健康产业约占美国经济总量的五分之一，Atlas-EHR为空间计算带来了极具吸引力的应用前景。然而，传统空间计算（如导航、土地测量、地图绘制）在生物医学领域面临重重障碍。本文围绕该主题，在空间计算的五大领域提出了若干待解决的开放性研究问题。