2SPD-026 Automated unit dose-dispensing device: assessment of the control method. (March 2019)

• Record Type: Journal Article
• Authors: Frémaux, P; Fiedler, A; Lelievre, J; Le Bot, M; Cogulet, V
• Is Part Of: European journal of hospital pharmacy. Volume 26(2019)Supplement 1
• Page Start: A30
• Page End: A31
• Publication Date: 2019-03
• Subjects: Pharmacy -- Periodicals; Hospital pharmacies -- Periodicals; 615.1
• DOI: 10.1136/ejhpharm-2019-eahpconf.66
• Languages: English
• ISSNs: 2047-9956

Abstract

Abstract : Background: In January 2018, an automated dispensing device was installed in our hospital (JVM slide type). It is a repackaging system in which oral solid forms are removed from the manufacturer's original packs and assembled into unit dose sachets. Our quality assurance programme consists in performing automatic inspection for filling completed sachets (JVM Vizen). Then, packages are automatically winded and cut (JVM Wizer). Non-conformities (NC) are classified by the technician into detection errors or real NC that are corrected afterwards. This two-step process is at risk because of human interventions. Purpose: The aim of the study was to assess the performance of the inspection machine, and ensure that the validation of the two-step process is correctly performed. Material and methods: During 12 weeks, all sachets have been analysed a posteriori, thanks to the photographs taken by the inspection machine (Vizen). The NC, as well as the validation errors, have been classified into detection errors (false positives) and real NC (i.e: missing drug, extra drug, foreign element in the sachet, broken drug, wrong medication). Results: 2 25 456 sachets have been produced since the beginning of the study: 8% were declared NC by the inspection machine: 81% of these NC were detection errors. Four drugs were frequently (25% of detection errors) recovered: Seresta 10 mg, 1/2 Alprazolam 0.25 mg, Ramipril 1.25 mg and 1/2 Seresta 50 mg. Eighteen per cent of the NC were real NC. Only 34 validation errors (i.e: NC correctly detected by the Vizen and wrongly classified as detection error by the technician) were observed. During the study, 13 NC were not detected by the inspection machine. Conclusion: Despite the automatic control, human intervention is required in this process. The staff will be alerted of those risks in order to raise their awareness and improve the validation step. The detection errors, which are very time-consuming, could also be decreased by enhancing the database of the inspection machine. The time saved could be used to focus more on the real NC. For extra and missing drugs, the container location could be modified. References and/or acknowledgements: https://www.sciencedirect.com/science/article/pii/S2211104215002507 No conflict of interest.