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Accuracy of antibacterial indication documentation in an electronic medicines management system
Wendy Yao1, Trisha N Peel2,3, Kelly A Cairns1
1Pharmacy Department, Alfred Health, Melbourne, Australia.
Introduction:
Electronic medicines management (EMM) systems are relatively new in the Australian healthcare system. This tertiary hospital network implemented an EMM in 2018, with mandatory documentation of antimicrobial indication when prescribing. Free-text (unrestricted) and pre-defined dropdown (restricted) indications are utilized according to antimicrobial restriction.
Objectives:
To determine accuracy of antibacterial indication documentation on the medication administration record (MAR) when prescribing and to evaluate factors influencing accuracy of documentation.
Methods:
A random sample of 400 inpatient admissions of ≥24 h, between March and September 2019, with the first antibacterial prescription per encounter reviewed retrospectively. Demographic and prescription details were extracted. Indication accuracy was assessed by comparing MAR documentation with the medical notes (gold standard). Statistical analysis compared factors associated with accuracy of indication using chi-squared and Fisher's exact tests.
Results:
Antibacterials were prescribed in 9708 admissions. Of the 400 patients included (60% male; median age 60 years, IQR 40-73), 225 prescriptions were unrestricted and 175 were restricted. Patients were managed by emergency (118), surgical (178) and medical (104) teams. Overall accuracy of antibacterial indication documentation on the MAR was 86%. A higher accuracy rate was found for the unrestricted proportion compared with the restricted proportion (94.2% versus 75.2%; P < 0.0001). Surgical teams had higher accuracy compared with medical and emergency teams (94.4% versus 78.8% versus 79.7%; P < 0.0001).
Discussion:
Antibacterial indication documentation on the MAR when prescribing demonstrated a high rate of accuracy. Multiple factors influenced this accuracy, which requires further study to determine the impact on accuracy, with a view to improve future EMM builds.
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