A flexible framework for visualizing and exploring patient misdiagnosis over time

Elsevier

Available online 2 September 2022, 104178

Journal of Biomedical InformaticsHighlights•

Understanding the patient diagnostic process over time (i.e., patient diagnosis paths) can provide key information and insights for improving clinical care. 

Lately, application of data visualization and visual analytics to healthcare data has become increasingly popular due to its usefulness.

We present a novel flexible visualization and analysis framework for exploring patient diagnosis paths.

Along with an interactive view of patient diagnosis paths, supplementary visualizations, various filtering and selection techniques are also integrated into our visualization system to allow quick exploration of different hypotheses.

Application of the system is demonstrated with a case study in healthcare to explore infection-based patient diagnosis paths.

Abstract

Diagnosis is a complex and ambiguous process and yet, it is the critical hinge point for all subsequent clinical reasoning and decision-making. Tracking the quality of the patient diagnostic process has the potential to provide valuable insights in improving the diagnostic accuracy and to reduce downstream errors but needs to be informative, timely, and efficient at scale. However, due to the rate at which healthcare data are captured on a daily basis, manually reviewing the diagnostic history of each patient would be a severely taxing process without efficient data reduction and representation. Application of data visualization and visual analytics to healthcare data is one promising approach for addressing these challenges. This paper presents a novel flexible visualization and analysis framework for exploring the patient diagnostic process over time (i.e., patient diagnosis paths). Our framework allows users to select a specific set of patients, events and/or conditions, filter data based on different attributes, and view further details on the selected patient cohort while providing an interactive view of the resulting patient diagnosis paths. A practical demonstration of our system is presented with a case study exploring infection-based patient diagnosis paths.

Keywords

Patient diagnosis paths

Data visualization

Visual analytics

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