Information Visualization Platform for Postmarket Surveillance Decision Support

Jonathan Spiker, Kory Kreimeyer, Oanh Dang, Debra Boxwell, Vicky Chan, Connie Cheng, Paula Gish, Allison Lardieri, Eileen Wu, Suranjan De, Jarushka Naidoo, Harold Lehmann, Gary L. Rosner, Robert Ball and Taxiarchis Botsis ()
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Jonathan Spiker: Johns Hopkins University School of Medicine
Kory Kreimeyer: Johns Hopkins University School of Medicine
Oanh Dang: Center for Drug Evaluation and Research, US FDA
Debra Boxwell: Center for Drug Evaluation and Research, US FDA
Vicky Chan: Center for Drug Evaluation and Research, US FDA
Connie Cheng: Center for Drug Evaluation and Research, US FDA
Paula Gish: Center for Drug Evaluation and Research, US FDA
Allison Lardieri: Center for Drug Evaluation and Research, US FDA
Eileen Wu: Center for Drug Evaluation and Research, US FDA
Suranjan De: Center for Drug Evaluation and Research, US FDA
Jarushka Naidoo: Johns Hopkins University School of Medicine
Harold Lehmann: Johns Hopkins University School of Medicine
Gary L. Rosner: Johns Hopkins University School of Medicine
Robert Ball: Center for Drug Evaluation and Research, US FDA
Taxiarchis Botsis: Johns Hopkins University School of Medicine

Drug Safety, 2020, vol. 43, issue 9, No 7, 905-915

Abstract: Abstract Introduction The US FDA receives more than 2 million postmarket reports each year. Safety Evaluators (SEs) review these reports, as well as external information, to identify potential safety signals. With the increasing number of reports and the size of external information, more efficient solutions for data integration and decision making are needed. Objectives The aim of this study was to develop an interactive decision support application for drug safety surveillance that integrates and visualizes information from postmarket reports, product labels, and biomedical literature. Methods We conducted multiple meetings with a group of seven SEs at the FDA to collect the requirements for the Information Visualization Platform (InfoViP). Using infographic design principles, we implemented the InfoViP prototype version as a modern web application using the integrated information collected from the FDA Adverse Event Reporting System, the DailyMed repository, and PubMed. The same group of SEs evaluated the InfoViP prototype functionalities using a simple evaluation form and provided input for potential enhancements. Results The SEs described their workflows and overall expectations around the automation of time-consuming tasks, including the access to the visualization of external information. We developed a set of wireframes, shared them with the SEs, and finalized the InfoViP design. The InfoViP prototype architecture relied on a javascript and a python-based framework, as well as an existing tool for the processing of free-text information in all sources. This natural language processing tool supported multiple functionalities, especially the construction of time plots for individual postmarket reports and groups of reports. Overall, we received positive comments from the SEs during the InfoViP prototype evaluation and addressed their suggestions in the final version. Conclusions The InfoViP system uses context-driven interactive visualizations and informatics tools to assist FDA SEs in synthesizing data from multiple sources for their case series analyses.

Date: 2020
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DOI: 10.1007/s40264-020-00945-0

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