How to prevent medication errors: a multidimensional scaling study to investigate the distinguishability between self-injection platform device variants. (3rd August 2019)

• Record Type: Journal Article
• Authors: Schneider, Andreas; Kolrep, Harald; Jordi, Christoph; Richard, Philipp; Horn, Hanns-Peter; Lange, Jakob
• Is Part Of: Expert opinion on drug delivery. Volume 16:Number 8(2019)
• Page Start: 883
• Page End: 894
• Publication Date: 2019-08-03
• Subjects: Subcutaneous injection -- self-medication -- drug delivery systems -- medication errors -- patient safety -- medical device design; Drug delivery devices -- Periodicals; Drug delivery systems -- Periodicals; 615.605
• DOI: 10.1080/17425247.2019.1637852
• Languages: English
• ISSNs: 1742-5247

Abstract

ABSTRACT: Background : The importance of subcutaneous drug delivery using self-injection devices based on common device platforms continues to grow. The resulting broad adoption of potentially look-alike or similar devices, however, raises concerns over limited device distinguishability and ensuing risk of medication errors. The objective of the study is thus to understand whether and how users effectively distinguish between self-injection device variants. Methods : Seventy-four patients, caregivers, and healthcare professionals were asked to pairwise rate the similarity of eight platform autoinjector variants. Multidimensional scaling was then used to convert individual ratings into spatial configurations and thereby identify the attributes that influence device distinguishability. Results : Five different device attributes driving distinguishability were identified. Three of the attributes corresponded to single design features (the label color, device size and device shape). Two device attributes (the aspect ratio and chromaticity) combined distinct yet interrelated design features. Conclusions : The study provides initial empirical evidence that users are able to distinguish between device variants and as to what device attributes drive distinguishability. Furthermore, the results highlight patterns in how various user groups distinguish between device variants. These patterns relate with the user group characteristics (e.g. age, sight or dexterity) and the context of device usage (e.g. healthcare professionals).