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BMC Med Inform Decis Mak. 2018 Oct 19;18(1):86. doi: 10.1186/s12911-018-0667-x.

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

Author information

1
INSERM, U1099, F-35000, Rennes, France. thibault.ledieu@gmail.com.
2
Université de Rennes 1, LTSI, F-35000, Rennes, France. thibault.ledieu@gmail.com.
3
INSERM, U1099, F-35000, Rennes, France.
4
Université de Rennes 1, LTSI, F-35000, Rennes, France.
5
CHU Rennes, F-35000, Rennes, France.
6
ERIAS, INSERM U897, ISPED, Université Bordeaux, Bordeaux, France.
7
Centre Régional de Pharmacovigilance, CHU Pontchaillou, Rennes, France.

Abstract

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.

KEYWORDS:

Informatics; Information visualization; Pharmacovigilance; Usability testing

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