Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Since the inception of electronic health records (EHR) and population health records (PopHR), the volume of archived digital health records is growing rapidly. The survey provides a valuable overview of EHR Vis revealing both mature areas and potential future multidisciplinary research directions. We classify the literature based on multidisciplinary research themes stemming from reoccurring topics. 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 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 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. 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. Large volumes of heterogeneous health records require advanced visualization and visual analytics systems to uncover valuable insight buried in complex databases. Domain expert partners from EHR analysis review the software and are involved in every phase from the initial design to evaluation. We demonstrate LetterVis with three case studies using anonymized clinic letters, revealing insight that is normally either time-consuming or impossible to observe. We provide a range of filtering and selection options to assist pattern finding and outlier detection. The tool includes customized visual designs and views for visualizing antiepileptic drugs (AEDs). Letters are processed using natural language processing techniques and explored in multiple linked interactive views providing different levels of abstraction. We describe a letter-space that facilities the visual exploration of content and patterns inside a letter. This paper presents a novel visualization tool, LetterVis, to support the analysis of clinic letters through advanced interactive visual designs and queries. This increases the workload of analyzing these letters, performing individual and collective analysis, and clinical decision making. Clinicians often compose detailed clinic letters to record as much essential information during consultations as they can. The number of electronic health records (EHRs) collected by healthcare providers is growing at an unprecedented pace.
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