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Evan Palmer, Logan Gisick; Visual Search for Medication Vials. Journal of Vision 2018;18(10):652. doi: 10.1167/18.10.652.
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When medical professionals administer an injectable drug, they must search for a medication vial that is typically small, visually indistinct, and stored near other similar looking medications. Every year, over 400,000 people die from medical errors and about one third of those deaths are medication-related. In a series of experiments, we investigated visual search for medication vials to determine which label properties lead to the quickest and most accurate search. In particular, we sought to determine whether features known to guide visual search in simple stimuli would also apply to these real-world stimuli. Using photo images of medication vials, we systematically varied the color density, text size, and text orientation of target vials. Participants were shown an image of the target medication vial for two seconds and then searched through a display of 12 vials, with six vials on the left and six on the right side of the screen. Participants reported which side of the screen contained the target vial, or if the target vial was absent from the display (10% of trials). In Experiment 1, participants were faster and more accurate to find target vials with high color density across all conditions. There was also an interaction such that when text orientation was vertical, small text was located faster, but when text orientation was horizontal, large text was located faster. In Experiment 2, we removed the glass bottles from the images and just displayed the labels alone to confirm that the results from Experiment 1 were due to label properties and not differences in the shapes of the bottles. We are currently investigating search efficiency for high color density vials with vertical vs. horizontal text orientation. These results indicate that visual attributes that efficiently guide search in basic science experiments also predict performance in search for medication vials.
Meeting abstract presented at VSS 2018
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