Abstract
Background: Medication errors are preventable events that lead to inappropriate medication use and potential patient harm. This is especially prevalent within the operating room (OR) where one practitioner is involved in the entire medication-use process. Despite recent implementation of BD Pyxis™ Anesthesia ES, Codonics Safe Label System, and Epic One Step at the University of Kentucky Healthcare (UKHC) to prevent medication errors, errors continue to be reported. Curatolo et al found human error was the most frequent cause of medication error within the OR. Clumsy automation may be an explanation for this, which imposes burdens and promotes work arounds. This study endeavors to assess potential medication errors via chart review to identify risk reduction strategies. Methods: This a single-center retrospective cohort review of patients admitted to a UK HealthCare Main Operating Room, defined OR1A-OR5A and OR7A-OR16A, who were administered medications from 8/1/2021 to 9/30/2021. Results: Over a 2-month period, 145 cases were conducted at UK HealthCare. Of the 145 cases, 98.6% (n = 143) involved a medication error and 93.7% (n = 136) of the errors involved a high-alert medication. The top 5 classes of drugs involved in errors were all high-alert medications. Lastly, 46.6% (n = 67) of cases had documentation that Codonics was utilized. In addition to analyzing medication errors, the financial analysis found that $3154.04 in drug cost was lost in the study period. When extrapolating these results to all BD™ Pyxis Anesthesia Machines at UK HealthCare, $107 237.36 of drug cost is potentially lost per year. Conclusions: These findings add to previous data that have described the increased rate of medication errors when conducting chart review rather than rely on self-reported data. In this study, 98.6% of all cases involved a medication error. In addition, these findings provide additional insight in the increased use of technology within the operating room despite medication errors still occurring. These results can be applied to like institutions to critically evaluate anesthesia workflow to determine risk reduction strategies.
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