Authors:
Tabassum Kakar
1
;
Xiao Qin
1
;
Cory M. Tapply
2
;
Oliver Spring
2
;
Derek Murphy
2
;
Daniel Yun
2
;
Elke A. Rundensteiner
2
;
Lane Harrison
2
;
Thang La
3
;
Sanjay K. Sahoo
3
and
Suranjan De
3
Affiliations:
1
Department of Computer Science, Worcester Polytechnic Institute, Worcester, Massachusetts, U.S.A., Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silverspring, Maryland and U.S.A.
;
2
Department of Computer Science, Worcester Polytechnic Institute, Worcester, Massachusetts and U.S.A.
;
3
Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silverspring, Maryland and U.S.A.
Keyword(s):
Visual Analytics, Treemaps, Drug Surveillance Reports, Pharmacovigilance.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Information and Scientific Visualization
;
Interactive Visual Interfaces for Visualization
;
Visual Representation and Interaction
;
Visualization Applications
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 co
rresponding 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.
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