Abstract
AbstractSince the inception of electronic health records (EHR) and population health records (PopHR), the volume of archived digital health records is growing rapidly. Large volumes of heterogeneous health records require advanced visualization and visual analytics systems to uncover valuable insight buried in complex databases. As a vibrant sub‐field of information visualization and visual analytics, many interactive EHR and PopHR visualization (EHR Vis) systems have been proposed, developed, and evaluated by clinicians to support effective clinical analysis and decision making. We present the state‐of‐the‐art (STAR) of EHR Vis literature and open access healthcare data sources and provide an up‐to‐date overview on this important topic. We identify trends and challenges in the field, introduce novel literature and data classifications, and incorporate a popular medical terminology standard called the Unified Medical Language System (UMLS). We provide a curated list of electronic and population healthcare data sources and open access datasets as a resource for potential researchers, in order to address one of the main challenges in this field. We classify the literature based on multidisciplinary research themes stemming from reoccurring topics. The survey provides a valuable overview of EHR Vis revealing both mature areas and potential future multidisciplinary research directions.
Highlights
Introduction and MotivationSeveral healthcare data institutes strive to exploit software-based technology to study and improve a nation’s collective health [Bus04, Act09, FJV*09, Lar14, Eur17]
We present a state-of-the-art (STAR) report of research literature focusing on visualization of Electronic Health Record (EHR) and Population Health Record (PopHR) to address these ongoing trends
We observe that standard 2D displays and glyph are the most popular techniques among 21 techniques found across all EHR and PopHR visualization (EHR Vis) systems. This implies that using advanced visual techniques to mitigate scalability challenge brought by EHR data dimensionality, remains understudied. In this STAR, we present an up-to-date overview of research papers, with an in-depth investigation of 99 in the field of EHR and PopHR Visualization and Visual Analytics
Summary
Several healthcare data institutes strive to exploit software-based technology to study and improve a nation’s collective health [Bus, Act, FJV*09, Lar, Eur17]. Due to the large volume of heterogeneous electronic healthcare data, researchers incorporate techniques such as Machine Learning (ML), Event Sequence Simplification (ESS) and Natural Language Processing (NLP) with interactive visualization and visual analytics, in order extract useful information enabling healthcare providers to obtain a more comprehensive understanding of the underlying patterns and behaviour related to health [SNLOB17]. We have developed an online EHR STAR literature browser for the readers: https://ehr.wangqiru.com It features all of the EHR papers and datasets along with several filtering and sorting options based on author, year, technique and search terms. Laramee / EHR STAR: The State-of-the-art in Interactive EHR Visualization
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