John Del Gaizo1, Ken R Catchpole2, Alexander V Alekseyenko1. 1. Biomedical Informatics Center, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, 29425, USA. 2. Department of Anesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, South Carolina, 29425, USA.
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.
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.
Authors: Ken R Catchpole; Elyse Hallett; Sam Curtis; Tannaz Mirchi; Colby P Souders; Jennifer T Anger Journal: Ergonomics Date: 2017-03-08 Impact factor: 2.778
Authors: Ken Catchpole; David M Neyens; James Abernathy; David Allison; Anjali Joseph; Scott T Reeves Journal: BMJ Qual Saf Date: 2017-09-28 Impact factor: 7.035
Authors: Douglas A Wiegmann; Andrew W ElBardissi; Joseph A Dearani; Richard C Daly; Thoralf M Sundt Journal: Surgery Date: 2007-11 Impact factor: 3.982
Authors: Ken Catchpole; Ann Bisantz; M Susan Hallbeck; Matthias Weigl; Rebecca Randell; Merrick Kossack; Jennifer T Anger Journal: Appl Ergon Date: 2018-03-02 Impact factor: 3.661