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

Abstract

Motivation: Research & Exploratory Analysis Driven Time-data Visualization (read-tv) is an open source R Shiny application for visualizing irregularly and regularly spaced longitudinal data. read-tv provides unique filtering and changepoint analysis (CPA) features. The need for these analyses was motivated by research of surgical work-flow disruptions in operating room settings. Specifically, for the analysis of the causes and characteristics of periods of high disruption-rates, which are associated with adverse surgical outcomes.

Materials and methods: read-tv is a graphical application, and the main component of a package of the same name. read-tv generates and evaluates code to filter and visualize data. Users can view the visualization code from within the application, which facilitates reproducibility. The data input requirements are simple, a table with a time column with no missing values. The input can either be in the form of a file, or an in-memory dataframe- which is effective for rapid visualization during curation.

Results: We used read-tv to automatically detect surgical disruption cascades. We found that the most common disruption type during a cascade was training, followed by equipment.

Discussion: read-tv fills a need for visualization software of surgical disruptions and other longitudinal data. Every visualization is reproducible, the exact source code that read-tv executes to create a visualization is available from within the application. read-tv is generalizable, it can plot any tabular dataset given the simple requirements that there is a numeric, datetime, or datetime string column with no missing values. Finally, the tab-based architecture of read-tv is easily extensible, it is relatively simple to add new functionality by implementing a tab in the source code.

Conclusion: read-tv enables quick identification of patterns through customizable longitudinal plots; faceting; CPA; and user-specified filters. The package is available on GitHub under an MIT license.

Keywords: R; Shiny; change point analysis; change-point analysis; changepoint analysis; forecasting; longitudinal visualization; surgical safety.

Figure 1.

Flow Disruption by Time after Surgery Start, Faceted. The filter and facet tab of read-tv supports faceting (Case), custom filters (Case > 10 and Relative Time < 380), different event colors (Event Type), shape (Phase), source code visualization, and other features shown above. This filtered and faceted data is passed to the CPA tab. The tabs to the right receive data from the tabs to the left.

Figure 2.

Source Code Generation. Every read-tv visualization is the result of code generated through meta programming techniques in response to user input. The code that creates the plot can be visualized by pressing the “Show Source” button. The code above will recreate the plot in Figure 1.

Figure 3.

CPA Tab. The CPA tab provides options to regularize timing intervals, prepare data for CPA input, and execute CPA algorithms. This is the same dataset as Figure 1, we just removed the custom filter in the “filter and facet tab” before calculating the changepoints.