Published on:September 2018
    Journal of Pharmacy Practice and Community Medicine, 2018; 4(3):150-154
    Research Article | doi:10.5530/jppcm.2018.3.35

    Analysis of Computer-Generated Drug Label Errors in a Tertiary Care Hospital

    Authors and affiliation (s):

    Ahmed AbdulRahim Abusham*, Hamida Rashid AlRawahi

    Department of Clinical Pharmacy, University of Nizwa College of Pharmacy and Nursing, Nizwa – PO Box 33 PC 616, OMAN.


    Background: Electronic Systems and recommendations have been developed to facilitate the medication dispensing process, but on the other hand, they may generate dispensing errors themselves. This study is meant to assess the computer-generated drug label errors (CGDLEs). Objective: The main objective of this study is to identify the pattern and assess the significance of CGDLEs in a tertiary care hospital. Methodology: A total of 292 computer-generated drug label errors involving 169 patients and 61 medications were researched over 3 months (from January 1, 2017, to Mar 30, 2017) in a 500-bed tertiary care hospital. Collected data included patient demographics, type of error, error-related medications, time taken to resolve the error, station where error was identified and the clinical significance of the error. Analyses were conducted using STATA® v14.2 with descriptive and inferential statistics. Results: Thirty eight percent of the detected CGDLEs were considered major. Patients age ranged between 0.2 to 97 years (M+SD=36.4+24.3). Errors within the age group of 0.2 to 2 years represented 7.89% of the total errors. Errors within the age group of 61 years and above represented 21.91% of the total errors. Duration of therapy represents 34.59% of the total errors, followed by instructions for use (29.45%) and drug dose (19.86%). The major CGDLEs commonly include medications like tacrolimus (17.69%), methotrexate (16.81%) and ciclosporin (15.40%). Conclusion: A considerable proportion of all CGDLEs was observed. Many of these errors were serious and could directly affect the wellbeing of patients. Fortunately, these errors were captured before reaching patients. Urgent assessment of the system that generates such labels is required.

    Key words: Computer-generated drug label, Dispensing errors, Medication errors.

    Download Article >>



    Cite this article as

    Abusham AA, AlRawahi HR. Analysis of Computer-Generated Drug Label Errors in a Tertiary Care Hospital. Journal of Pharmacy Practice and Community Medicine. 2018;4(3):150-4. Abstract