CP-226 Prescriptions analysis: how can we target our work?. (25th February 2017)

• Record Type: Journal Article
• Authors: Stordeur, F; Khouri, T; Lehrer, J; Beaussier, H; Bezie, Y; Thi, TT Phan
• Is Part Of: European journal of hospital pharmacy. Volume 24(2017)Supplement 1
• Page Start: A101
• Page End: A102
• Publication Date: 2017-02-25
• Subjects: Pharmacy -- Periodicals; Hospital pharmacies -- Periodicals; 615.1
• DOI: 10.1136/ejhpharm-2017-000640.224
• Languages: English
• ISSNs: 2047-9956

Abstract

Abstract : Background: Pharmacists have to dedicate a significant amount of their time to prescriptions analysis; however, some prescriptions may present less risk than others. Purpose: To evaluate focusing pharmaceutical validation on prescriptions containing one narrow therapeutic range drug (NTRD). Material and methods: For 2 months, prescription analysis (DxCare) was performed by two pharmacists. The tested group (TG) collected every prescription that included at least one NTRD (vitamin K antagonists, oral cancer drugs, immunosuppressant drugs and antiretroviral therapies) in all the departments that were computerised. Prescriptions for a single department every week (neurology, cardiology, vascular medicine and internal medicine) formed the control group (CG). Pharmacists analysed prescriptions from one group for 1 week and from the other group on the following week. PI were categorised according to relevance and to the French Clinical Pharmaceutical Society classification, and time spent was recorded. Results: 956 prescriptions were analysed. The first three causes of PI were non-conformity to a referential and contraindication (24.2%), overdose (18.5%) and omissions (16.9%). However, most relevant PI were due to interactions (13.7%) and involved mainly cardiovascular medicines (45.5%). A modification of the prescription was recorded for 60.0% of the 55 relevant PI. 478 prescriptions were analysed in each group. Compared with the CG, prescriptions from the TG contained more medicines (12.4±5.0 vs 10.1±4.5; p<0.001) and analysis required more time (0.35 vs 0.31 min/line of treatment; p<0.001). 73 PI were observed in the TG versus 51 for the CG (p<0.01), and relevant IP concerned mainly omissions (23.5%, TG) and interactions (42.8%, CG). No PI was related to an NTRD in the CG, whereas in the TG, 16 PI were related (22.0%). Conclusion: Targeting of drugs helps to select prescriptions which have a higher risk of error. However, most IP are not related to an NTRD. Patients in the TG were often suffering from multiple pathologies in parallel with the one for which they are treated with an NTRD, and the prescription of these other drugs is often forgotten. Further studies should include analysis of demographic characteristics to define additional criteria that should be taken into account for targeting prescriptions. References and/or acknowledgements: SFPC 2015. No conflict of interest