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Spatial Computing Opportunities in Biomedical Decision Support: The Atlas-EHR Vision

Majid Farhadloo, Arun Sharma, Shashi Shekhar, Svetomir N. Markovic

TL;DR

This paper addresses the time burden clinicians face in interpreting long patient histories by proposing Atlas-EHR, a spatial, anatomy-grounded 4D representation of EHRs and biomedical data. It surveys five spatial computing domains and discusses emerging inner-space data (spatial omics, cell atlases) and the needed data models, AI, and visualization tools to support immersive, explainable decision support. It emphasizes challenges such as non-rigid anatomy, data scale, privacy, and fairness, and presents open research opportunities in spatial DBMS, pattern mining, positioning, sensing, and cartography. The work argues that Atlas-EHR can enable precision medicine, real-time monitoring, and spatially-aware interventions, potentially transforming healthcare delivery.

Abstract

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.

Spatial Computing Opportunities in Biomedical Decision Support: The Atlas-EHR Vision

TL;DR

This paper addresses the time burden clinicians face in interpreting long patient histories by proposing Atlas-EHR, a spatial, anatomy-grounded 4D representation of EHRs and biomedical data. It surveys five spatial computing domains and discusses emerging inner-space data (spatial omics, cell atlases) and the needed data models, AI, and visualization tools to support immersive, explainable decision support. It emphasizes challenges such as non-rigid anatomy, data scale, privacy, and fairness, and presents open research opportunities in spatial DBMS, pattern mining, positioning, sensing, and cartography. The work argues that Atlas-EHR can enable precision medicine, real-time monitoring, and spatially-aware interventions, potentially transforming healthcare delivery.

Abstract

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.
Paper Structure (20 sections, 9 figures, 2 tables)

This paper contains 20 sections, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Challenges and Opportunities: Technological Evolution in EHR and Spatial Computing.
  3. Background & Emerging Spatial Data from Inner Space
  4. Brief Overview of Spatial Computing
  5. Spatial Data at the Scale of Inner Space
  6. Accomplishments and Open Research Opportunities in Inner Space
  7. Spatial Database Management systems
  8. Spatial Pattern Mining
  9. Positioning, Navigation, Location-based services
  10. Sensing
  11. Cartography and Visualization
  12. Discussion: Bridging the Physical and Digital Space with Atlas-EHR
  13. Conclusion & Future Work
  14. Overview of Existing EHR Platform
  15. Spatial Computing in Geographic Space
  16. ...and 5 more sections

Figures (9)

  • Figure 1: A spatially-organized map of electronic health records
  • Figure 2: Spatial maps of the human circulatory system, illustrating the flow and distribution of blood to various organs.
  • Figure 3: A virtual H&E tissue slide including pathology-driven fields of view namely, infiltrate, interface, tumor, and normal maus2022resolving.
  • Figure 4: (a) Multi-category point sets of the responder and non-responder (b) Point patterns detailing spatial arrangements between Tumor cells, Neutrophils, and Macrophages. (c) A spatially-explainable decision tree to distinguish between responder and non-responder at the tumor core
  • Figure 5: Three pathology-driven fields of view are categorized based on tumor infiltration
  • ...and 4 more figures