Abstract
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. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work.
Highlights
In recent years, medical organizations are increasingly deploying electronic health record (EHR)-based systems that generate, store, and manage their data
As the amount of data stored in EHRs continues to grow exponentially, and new EHR-based systems are implemented for those already overrun with too much data, there is a growing demand for computational systems that can handle the huge amount of clinical data
We identify four categories of visualizations that are commonly used in EHR-based Visual analytics (VA) systems: (1) Relation-based, (2) time-based, (3) hierarchy-based, and (4) flow-based visualizations
Summary
Medical organizations are increasingly deploying electronic health record (EHR)-based systems that generate, store, and manage their data. The amount of data available to clinical researchers and clinicians continues to grow at an unprecedented rate, creating an untapped resource with the capacity to improve the healthcare system [1]. Despite the evidence showing the benefits of EHR-based systems, they rarely improve healthcare experts’ ability to make better clinical decisions by having access to more comprehensive information [9,10]. As the amount of data stored in EHRs continues to grow exponentially, and new EHR-based systems are implemented for those already overrun with too much data, there is a growing demand for computational systems that can handle the huge amount of clinical data
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