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Effect of pharmacy admission services on medication charting activities: A retrospective cohort study
Huri Balikubiri1, Anna Kemp-Casey2, Andre Q Andrade2
1University of South Australia, Adelaide, SA, and SA Pharmacy, SA Health, Adelaide, SA, Australia.
Purpose:
Medication omissions are the most common charting error at hospital admission. Pharmacy admission services, including a best possible medication history, reconciliation, and review, have reduced the incidence of these errors in controlled trials. This real-world study assessed how the timing of pharmacy admission service provision influences early medication prescribing practices in hospitals.
Methods:
A retrospective cohort study was conducted using electronic health record data from 3 metropolitan hospitals (May to November 2021). Adult general medicine admissions were grouped by the timing of pharmacy service delivery relative to initial medication charting: before, within 24 hours of, more than 24 hours after, or not provided. Medication charting activities were coded to identify additions, stops, modifications, and rapid retractions within 24 hours of the start of inpatient medication charting. Negative binomial regression was used to examine associations between service timing and charting activities, with incidence rate ratios (IRRs) and 95% confidence intervals reported. Admissions in which pharmacy services were delivered before medication charting were the reference group.
Results:
Among 4,149 admissions (3,649 patients), the provision of pharmacy services before charting was associated with more initial medications being charted (P < 0.05) and fewer subsequent additions (P < 0.001). Service provision within 24 hours was associated with higher rates of additions (IRR = 1.83; P < 0.001) and modifications (IRR = 1.51; P = 0.004). No significant differences were observed in medication stops or rapid retractions across the groups.
Conclusion:
Pharmacy admission services influence medication charting practices on admission. The nature of influence can vary according to the timing of services and can be measured using routinely collected data.
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