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Timeline representation of clinical data: usability and added value for pharmacovigilance

BACKGROUND: Pharmacovigilance consists in monitoring and preventing the occurrence of adverse drug reactions (ADR). This activity requires the collection and analysis of data from the patient record or any other sources to find clues of a causality link between the drug and the ADR. This can be time...

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Autores principales: Ledieu, Thibault, Bouzillé, Guillaume, Thiessard, Frantz, Berquet, Karine, Van Hille, Pascal, Renault, Eric, Polard, Elisabeth, Cuggia, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6194681/
https://www.ncbi.nlm.nih.gov/pubmed/30340483
http://dx.doi.org/10.1186/s12911-018-0667-x
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author Ledieu, Thibault
Bouzillé, Guillaume
Thiessard, Frantz
Berquet, Karine
Van Hille, Pascal
Renault, Eric
Polard, Elisabeth
Cuggia, Marc
author_facet Ledieu, Thibault
Bouzillé, Guillaume
Thiessard, Frantz
Berquet, Karine
Van Hille, Pascal
Renault, Eric
Polard, Elisabeth
Cuggia, Marc
author_sort Ledieu, Thibault
collection PubMed
description BACKGROUND: Pharmacovigilance consists in monitoring and preventing the occurrence of adverse drug reactions (ADR). This activity requires the collection and analysis of data from the patient record or any other sources to find clues of a causality link between the drug and the ADR. This can be time-consuming because often patient data are heterogeneous and scattered in several files. To facilitate this task, we developed a timeline prototype to gather and classify patient data according to their chronology. Here, we evaluated its usability and quantified its contribution to routine pharmacovigilance using real ADR cases. METHODS: The timeline prototype was assessed using the biomedical data warehouse eHOP (from entrepôt de données biomédicales de l’HOPital) of the Rennes University Hospital Centre. First, the prototype usability was tested by six experts of the Regional Pharmacovigilance Centre of Rennes. Their experience was assessed with the MORAE software and a System and Usability Scale (SUS) questionnaire. Then, to quantify the timeline contribution to pharmacovigilance routine practice, three of them were asked to investigate possible ADR cases with the “Usual method” (analysis of electronic health record data with the DxCare software) or the “Timeline method”. The time to complete the task and the data quality in their reports (using the vigiGrade Completeness score) were recorded and compared between methods. RESULTS: All participants completed their tasks. The usability could be considered almost excellent with an average SUS score of 82.5/100. The time to complete the assessment was comparable between methods (P = 0.38) as well as the average vigiGrade Completeness of the data collected with the two methods (P = 0.49). CONCLUSIONS: The results showed a good general level of usability for the timeline prototype. Conversely, no difference in terms of the time spent on each ADR case and data quality was found compared with the usual method. However, this absence of difference between the timeline and the usual tools that have been in use for several years suggests a potential use in pharmacovigilance especially because the testers asked to continue using the timeline after the evaluation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-018-0667-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-61946812018-10-25 Timeline representation of clinical data: usability and added value for pharmacovigilance Ledieu, Thibault Bouzillé, Guillaume Thiessard, Frantz Berquet, Karine Van Hille, Pascal Renault, Eric Polard, Elisabeth Cuggia, Marc BMC Med Inform Decis Mak Software BACKGROUND: Pharmacovigilance consists in monitoring and preventing the occurrence of adverse drug reactions (ADR). This activity requires the collection and analysis of data from the patient record or any other sources to find clues of a causality link between the drug and the ADR. This can be time-consuming because often patient data are heterogeneous and scattered in several files. To facilitate this task, we developed a timeline prototype to gather and classify patient data according to their chronology. Here, we evaluated its usability and quantified its contribution to routine pharmacovigilance using real ADR cases. METHODS: The timeline prototype was assessed using the biomedical data warehouse eHOP (from entrepôt de données biomédicales de l’HOPital) of the Rennes University Hospital Centre. First, the prototype usability was tested by six experts of the Regional Pharmacovigilance Centre of Rennes. Their experience was assessed with the MORAE software and a System and Usability Scale (SUS) questionnaire. Then, to quantify the timeline contribution to pharmacovigilance routine practice, three of them were asked to investigate possible ADR cases with the “Usual method” (analysis of electronic health record data with the DxCare software) or the “Timeline method”. The time to complete the task and the data quality in their reports (using the vigiGrade Completeness score) were recorded and compared between methods. RESULTS: All participants completed their tasks. The usability could be considered almost excellent with an average SUS score of 82.5/100. The time to complete the assessment was comparable between methods (P = 0.38) as well as the average vigiGrade Completeness of the data collected with the two methods (P = 0.49). CONCLUSIONS: The results showed a good general level of usability for the timeline prototype. Conversely, no difference in terms of the time spent on each ADR case and data quality was found compared with the usual method. However, this absence of difference between the timeline and the usual tools that have been in use for several years suggests a potential use in pharmacovigilance especially because the testers asked to continue using the timeline after the evaluation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-018-0667-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-19 /pmc/articles/PMC6194681/ /pubmed/30340483 http://dx.doi.org/10.1186/s12911-018-0667-x Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Ledieu, Thibault
Bouzillé, Guillaume
Thiessard, Frantz
Berquet, Karine
Van Hille, Pascal
Renault, Eric
Polard, Elisabeth
Cuggia, Marc
Timeline representation of clinical data: usability and added value for pharmacovigilance
title Timeline representation of clinical data: usability and added value for pharmacovigilance
title_full Timeline representation of clinical data: usability and added value for pharmacovigilance
title_fullStr Timeline representation of clinical data: usability and added value for pharmacovigilance
title_full_unstemmed Timeline representation of clinical data: usability and added value for pharmacovigilance
title_short Timeline representation of clinical data: usability and added value for pharmacovigilance
title_sort timeline representation of clinical data: usability and added value for pharmacovigilance
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6194681/
https://www.ncbi.nlm.nih.gov/pubmed/30340483
http://dx.doi.org/10.1186/s12911-018-0667-x
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