  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.
