Detection of Patients at High Risk of Medication Errors: Development and Validation of an Algorithm. Issue 2 (22nd September 2015)

• Record Type: Journal Article
• Authors: Saedder, Eva Aggerholm; Lisby, Marianne; Nielsen, Lars Peter; Rungby, Jørgen; Andersen, Ljubica Vukelic; Bonnerup, Dorthe Krogsgaard; Brock, Birgitte
• Is Part Of: Basic & clinical pharmacology & toxicology. Volume 118:Issue 2(2016)
• Page Start: 143
• Page End: 149
• Publication Date: 2015-09-22
• Subjects: Pharmacology -- Periodicals; Toxicology -- Periodicals; Pharmacology -- Periodicals; Toxicology -- Periodicals; Pharmacology, Clinical -- Periodicals; Computer network resources; Electronic journals; 615.1
• DOI: 10.1111/bcpt.12473
• Languages: English
• ISSNs: 1742-7835

Abstract

Abstract: Medication errors (MEs) are preventable and can result in patient harm and increased expenses in the healthcare system in terms of hospitalization, prolonged hospitalizations and even death. We aimed to develop a screening tool to detect acutely admitted patients at low or high risk of MEs comprised by items found by literature search and the use of theoretical weighting. Predictive variables used for the development of the risk score were found by the literature search. Three retrospective patient populations and one prospective pilot population were used for modelling. The final risk score was evaluated for precision by the use of sensitivity, specificity and area under the ROC (receiver operating characteristic) curves. The variables used in the final risk score were reduced renal function, the total number of drugs and the risk of individual drugs to cause harm and drug–drug interactions. We found a risk score in the prospective population with an area under the ROC curve of 0.76. The final risk score was found to be quite robust as it showed an area under the ROC curve of 0.87 in a recent patient population, 0.74 in a population of internal medicine and 0.66 in an orthopaedic population. We developed a simple and robust score, MERIS, with the ability to detect patients and divide them according to low and high risk of MEs in a general population admitted at acute admissions unit. The accuracy of the risk score was at least as good as other models reported using multiple regression analysis.