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Designing a Visual Analytics System for Medication Error Screening and Detection

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Book cover Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1182))

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Abstract

Drug safety analysts at the U.S. Food & Drug Administration analyze medication error reports submitted to the Adverse Event Reporting System (FAERS) to detect and prevent detrimental errors from happening in the future. Currently this review process is time-consuming, involving manual extraction and sense-making of the key information from each report narrative. There is a need for a visual analytics approach that leverages both computational techniques and interactive visualizations to empower analysts to quickly gain insights from reports. To assist analysts responsible for identifying medication errors in these reports, we design an interactive Medication Error Visual analytics (MEV) system. In this paper, we describe the detailed study of the Pharmacovigilance at the FDA and the iterative design process that lead to the final design of MEV technology. MEV a multi-layer treemap based visualization system, guides analysts towards the most critical medication errors by displaying interactive reports distributions over multiple data attributes such as stages, causes and types of errors. A user study with ten drug safety analysts at the FDA confirms that screening and review tasks performed with MEV are perceived as being more efficient as well as easier than when using their existing tools. Expert subjective interviews highlight opportunities for improving MEV and the utilization of visual analytics techniques in general for analyzing critical FAERS reports at scale.

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Author information

Authors and Affiliations

  1. Worcester Polytechnic Institute, Worcester, MA, 01609, USA

    Tabassum Kakar, Xiao Qin, Cory M. Tapply, Oliver Spring, Derek Murphy, Daniel Yun, Elke A. Rundensteiner & Lane Harrison

  2. U.S. Food and Drug Administration, Silverspring, MD, 20993, USA

    Thang La, Sanjay K. Sahoo & Suranjan De

Authors
  1. Tabassum Kakar
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  2. Xiao Qin
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  3. Cory M. Tapply
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  4. Oliver Spring
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  5. Derek Murphy
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  6. Daniel Yun
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  7. Elke A. Rundensteiner
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  8. Lane Harrison
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  9. Thang La
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  10. Sanjay K. Sahoo
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  11. Suranjan De
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Corresponding author

Correspondence to Tabassum Kakar .

Editor information

Editors and Affiliations

  1. University of Lisbon, Lisbon, Portugal

    Ana Paula Cláudio

  2. University of Rennes 1, Rennes, France

    Kadi Bouatouch

  3. University of Genoa, Genoa, Italy

    Manuela Chessa

  4. Mines ParisTech, Paris, France

    Alexis Paljic

  5. Linnaeus University, Växjö, Sweden

    Andreas Kerren

  6. French Civil Aviation University (ENAC), Toulouse, France

    Christophe Hurter

  7. University Jean Monnet, Saint-Etienne, France

    Alain Tremeau

  8. University of Catania, Catania, Italy

    Giovanni Maria Farinella

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Kakar, T. et al. (2020). Designing a Visual Analytics System for Medication Error Screening and Detection. In: Cláudio, A., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2019. Communications in Computer and Information Science, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-41590-7_12

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  • DOI: https://doi.org/10.1007/978-3-030-41590-7_12

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-41590-7

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