Literature DB >> 28549568

Using visual analytics for presenting comparative information on new drugs.

Jean-Baptiste Lamy1, Hélène Berthelot2, Madeleine Favre3, Adrien Ugon4, Catherine Duclos5, Alain Venot6.   

Abstract

OBJECTIVE: When a new drug is marketed, physicians must decide whether they will consider it for their future practice. However, information about new drugs can be biased or hard to find. In this work, our objective was to study whether visual analytics could be used for comparing drug properties such as contraindications and adverse effects, and whether this visual comparison can help physicians to forge their own well-founded opinions about a new drug.
MATERIALS AND METHODS: First, an ontology for comparative drug information was designed, based on the expectations expressed during focus groups comprised of physicians. Second, a prototype of a visual drug comparator website was developed. It implements several visualization methods: rainbow boxes (a new technique for overlapping set visualization), dynamic tables, bar charts and icons. Third, the website was evaluated by 22 GPs for four new drugs. We recorded the general satisfaction, the physician's decision whether to consider the new drug for future prescription, both before and after consulting the website, and their arguments to justify their choice.
RESULTS: The prototype website permits the visual comparison of up to 10 drugs, including efficacy, contraindications, interactions, adverse effects, prices, dosage regimens,…All physicians found that the website allowed them to forge a well-founded opinion on the four new drugs. The physicians changed their decision about using a new drug in their future practice in 29 cases (out of 88) after consulting the website. DISCUSSION AND
CONCLUSION: Visual analytics is a promising approach for presenting drug information and for comparing drugs. The visual comparison of drug properties allows physicians to forge their opinions on drugs. Since drug properties are available in reference texts, reviewed by public health agencies, it could contribute to the independent of drug information.
Copyright © 2017. Published by Elsevier Inc.

Keywords:  Drug information; Information visualization; New drugs; Visual analytics

Mesh:

Year:  2017        PMID: 28549568     DOI: 10.1016/j.jbi.2017.04.019

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  2 in total

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Authors:  Sylvia Pelayo; Jacques Bouaud; Claudia Blancafort; Jean-Baptiste Lamy; Booma Devi Sekar; Nekane Larburu; Naiara Muro; Ander Urruticoechea Ribate; Jon Belloso; Guillermo Valderas; Sara Guardiola; Charlotte Ngo; Luis Teixeira; Gilles Guézennec; Brigitte Séroussi
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

2.  MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection.

Authors:  Chen He; Luana Micallef; Zia-Ur-Rehman Tanoli; Samuel Kaski; Tero Aittokallio; Giulio Jacucci
Journal:  BMC Bioinformatics       Date:  2017-09-13       Impact factor: 3.169

  2 in total

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