MEV: Visual Analytics for Medication Error Detection

Tabassum Kakar, Xiao Qin, Cory M. Tapply, Oliver Spring, Derek Murphy, Daniel Yun, Elke A. Rundensteiner, Lane Harrison, Thang La, Sanjay K. Sahoo, Suranjan De

2019

Abstract

To detect harmful medication errors and inform regulatory actions, the U.S. Food & Drug Administration uses the FAERS spontaneous reporting system to collect medication error reports. Drug safety analysts, however, review the submitted report narratives one by one to pinpoint critical medication errors. Based on a formative study of the review process requirements, we design an interactive visual analytics prototype called Medication Error Visual analytics (MEV), to facilitate the medication error review process. MEV visualizes distributions of the reports over multiple data attributes such as products, types of error, etc., to guide analysts towards most concerning medication errors. MEV supports interactive filtering on key data attributes that aim to help analysts hone in on the set of evidential reports. A multi-layer treemap visualizes the count and severity of the errors conveyed in the underlying reports, while the interaction between these layers aid in the analysis of the corresponding data attributes and their relationships. The results of a user study conducted with analysts at the FDA suggests that participants are able to perform the essential screening and review tasks more quickly with MEV and perceive tasks as being easier with MEV than with their existing tool set. Post-study qualitative interviews illustrates analysts’ interest in the use of visual analytics for FAERS reports analysis operations, opportunities for improving the capabilities of MEV, and new directions for analyzing critical spontaneous reports at scale.

Paper Citation

in Harvard Style

Kakar T., Qin X., Tapply C., Spring O., Murphy D., Yun D., Rundensteiner E., Harrison L., La T., Sahoo S. and De S. (2019). MEV: Visual Analytics for Medication Error Detection. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 3: IVAPP; ISBN 978-989-758-354-4, SciTePress, pages 72-82. DOI: 10.5220/0007366200720082

in Bibtex Style

@conference{ivapp19,
author={Tabassum Kakar and Xiao Qin and Cory M. Tapply and Oliver Spring and Derek Murphy and Daniel Yun and Elke A. Rundensteiner and Lane Harrison and Thang La and Sanjay K. Sahoo and Suranjan De},
title={MEV: Visual Analytics for Medication Error Detection},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 3: IVAPP},
year={2019},
pages={72-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007366200720082},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 3: IVAPP
TI - MEV: Visual Analytics for Medication Error Detection
SN - 978-989-758-354-4
AU - Kakar T.
AU - Qin X.
AU - Tapply C.
AU - Spring O.
AU - Murphy D.
AU - Yun D.
AU - Rundensteiner E.
AU - Harrison L.
AU - La T.
AU - Sahoo S.
AU - De S.
PY - 2019
SP - 72
EP - 82
DO - 10.5220/0007366200720082
PB - SciTePress