Welcome to the Surgical Glove Defect Visualization Site
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The data for this model was obtained from:
Dr. Denise M. Korniewicz RN, FAAN Professor
University of Maryland School of Nursing School of Medicine
Glove model created by:
Jennifer Seevinck, Research Scientist
Virginia Modeling, Analysis, and Simulation Center (VMASC) East
Old Dominion University
A spreadsheet of defect data is used to map the data to a color saturation scale.
Sheet1: Raw data.
Sheet2: Refers to sheet 1's data to average surgeon defects (Attending and Resident) for one set of glove models, and average assistants defects (First Assistant and Scrub) for the other set of glove models.
(Please contact POC for access to spreadsheet)
The area-specific defect data are visualized in each of the following sets of glove models:
Attending and Resident Surgeons
Left Inner Glove
Right Inner Glove
Left Outer Glove
Right Outer Glove
First Assistant and Scrub
Left Inner Glove
Right Inner Glove
Left Outer Glove
Right Outer Glove
Areas of high red color saturation connote high number of defects, low saturation connote a low number of defects. A maximum saturation level (the brightest red) represents the average highest number of defects at any site, i.e. the data range scales the color saturation range. The average maximum defects at any site is 47 on all gloves. This value is collected from the data sets and can be changed to update a new set of color values. (Sheet 2, Cell I3 )
At the moment all glove models share the same color scale. This allows one to perceive large scale trends, such as the greater number of defects in the outer gloves as opposed to the inner gloves. However, this does not enable a clear perception of areas where there is a small change, such as the difference between areas where no defects have occurred and those areas where a small number of defects have occurred, as occurs on the inner gloves. For this reason the spreadsheet has been set up to enable one to easily rescale the inner gloves color values relative to the average maximum number of inner glove defects, 5.5 (compared to 47 in the outer gloves. Sheet 2, Cell I4). An ability to "zoom" into the inner gloves to view such a color mapping could be a useful strategy for future more detailed visualizations.
Although the present averaged visualization models indicate defect trends in certain areas and gloves, a more detailed study could benefit from visualizing all 4 Health Care Workers (HCW) separately. For example, the averaged assistants data does not reflect the high number of defects in the scrub relative to the first assistant. Furthermore, the maximum number of defects at any site is 70 whereas the average maximum number of defects is 47.