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Research and Exploratory Analysis Driven-Time-data Visualization (read-tv) software.

John Del Gaizo1, Ken R Catchpole2, Alexander V Alekseyenko1

  • 1Biomedical Informatics Center, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, 29425, USA.

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|March 12, 2021
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Summary
This summary is machine-generated.

The read-tv R Shiny application visualizes longitudinal data and identifies surgical workflow disruptions. It found training and equipment were the most common causes of disruption cascades.

Keywords:
RShinychange point analysischange-point analysischangepoint analysisforecastinglongitudinal visualizationsurgical safety

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Area of Science:

  • Data Visualization
  • Bioinformatics
  • Medical Informatics

Background:

  • Longitudinal data visualization is crucial for analyzing complex datasets, particularly in healthcare research.
  • Surgical workflow disruptions are associated with adverse patient outcomes, necessitating effective analysis tools.
  • Existing tools often lack specialized features for analyzing time-series data with disruptions.

Purpose of the Study:

  • To introduce Research & Exploratory Analysis Driven Time-data Visualization (read-tv), an open-source R Shiny application.
  • To provide unique filtering and changepoint analysis (CPA) features for longitudinal data.
  • To facilitate the analysis of surgical workflow disruptions and their characteristics.

Main Methods:

  • read-tv is an R package featuring a graphical application for generating and evaluating data filtering and visualization code.
  • It accepts tabular data with a time column, supporting file or in-memory dataframe inputs.
  • Users can view the generated visualization code for enhanced reproducibility.

Main Results:

  • read-tv was utilized to automatically detect surgical disruption cascades.
  • The analysis revealed that 'training' was the most frequent disruption type during cascades, followed by 'equipment' issues.
  • The application enables pattern identification through customizable plots, faceting, CPA, and user-defined filters.

Conclusions:

  • read-tv addresses the need for specialized visualization software for surgical disruptions and other longitudinal data.
  • The application ensures reproducibility, generalizability to various tabular datasets, and extensibility for new functionalities.
  • read-tv is available on GitHub under an MIT license, promoting open-source collaboration.