Background There is a paucity of quantitative evidence in the current literature on the incidence of wrong medication and wrong dose administration of intravenous medications by clinicians. The difficulties of obtaining reliable data are related to the fact that at this stage of the medication administration chain, detection of errors is extremely difficult. Smart pump medication library logs and their reporting software record medication and dose selections made by users, as well as cancellations of selections and the time between these actions. Analysis of these data adds quantitative data to the detection of these kinds of errors. Objective We aimed to establish, in a reproducible and reliable study, baseline data to show how metrics in the set-up and programming phase of intravenous medication administration can be produced from medication library near-miss error reports from infusion pumps. Methods We performed a 12-month retrospective review of medication library reports from infusion pumps from across a facility to obtain metrics on the set-up phase of intravenous medication administration. Cancelled infusions and resolutions of all infusion alerts by users were analyzed. Decision times of clinicians were calculated from the time-date stamps of the pumps’ logs. Results Incorrect medication selections represented 3.45% (10,017/290,807) of all medication library alerts and 22.40% (10,017/44,721) of all cancelled infusions. Of these cancelled medications, all high-risk medications, oncology medications, and all intravenous medications delivered to pediatric patients and neonates required a two-nurse check according to the local policy. Wrong dose selection was responsible for 2.93% (8533/290,807) of all alarms and 19.08% (8533/44,721) of infusion cancellations. Average error recognition to cancellation and correction times were 27.00 s (SD 22.25) for medication error correction and 26.52 s (SD 24.71) for dose correction. The mean character count of medications corrected from initial lookalike-soundalike selection errors was 13.04, with a heavier distribution toward higher character counts. The position of the word/phrase error was spread among name beginning (6991/10,017, 69.79%), middle (2144/10,017, 21.40%), and end (882/10,017, 8.80%). Conclusions The study identified a high number of lookalike-soundalike near miss errors, with cancellation of one medication being rapidly followed by the programming of a second. This phenomenon was largely centered on initial misreadings of the beginning of the medication name, with some incidences of misreading in the middle and end portions of medication nomenclature. The value of an infusion pump showing the entire medication name complete with TALLman lettering on the interface matching that of medication labeling is supported by these findings. The study provides a quantitative appraisal of an area that has been resistant to study and measurement, which is the number of intravenous medication administration errors of wrong medication and wrong dose that occur in clinical settings.
BACKGROUND There is a paucity of quantitative evidence in the current literature on the incidence of wrong-medication and wrong-dose administration of intravenous medications by clinicians. The difficulties of obtaining reliable data in this area are related to the fact that at this stage of the medication administration chain detection of error becomes extremely difficult. Smart pump medication library logs, and their reporting software, record medication and dose selections made by users, as well as cancellations of selections and the time between these actions. Analysis of this data adds quality quantitative data to the detection of these kinds of error. OBJECTIVE To establish, in a reproducible and reliable study, baseline data to show how metrics on the set-up and initial programming phase of intravenous medication administration can be produced from review of medication library near-miss error reports from infusion pumps. METHODS A twelve-month retrospective review of medication library reports from infusion pumps used in 15 disciplines across a facility obtained metrics on the set-up phase of intravenous medication administration. Cancelled infusions and resolutions of all infusion alerts by users were analysed. Decision times of clinicians were calculated from the time-date stamps of the pumps’ logs. RESULTS Incorrect medication selections were 3.45% of all medication library alerts and 22.4% of all cancelled infusions. Of these cancelled medications all high-risk medications, oncology medications, and all IV medications delivered to paediatrics and neonates, would require a two-nurse check according to local policy. Wrong dose selection was responsible for 2.93% of all alarms and 19.08% of infusion cancellations. Average error recognition to cancellation and correction time was 27 seconds (22.25) for medication error correction, and 26.52 seconds (24.71) for dose corrections. The mean average character count of medications corrected from initial lookalike-soundalike selection errors was 13.04, with a heavier distribution towards higher character counts. In terms of the position of the word/phrase error, this was spread between name beginning (69.8%), name middle (21.4%), and name end (8.8%). CONCLUSIONS The study identified a significant number of lookalike-soundalike near miss errors, with cancellations of one medication being rapidly followed by the programming of a second. This phenomenon was found to be largely centred on initial mis-readings of the beginning of the medication name, but also showed some incidences of mis-reading in the middle and end portions of medication nomenclature. The value of an infusion pump having the capability to show the entire medication name, complete with TALLman lettering on the interface matching that of medication labelling is supported by these findings. The study provides a quantitative appraisal of an area that has been resistant to study and measurement; the number of intravenous medication administration errors of wrong medication and wrong dose that occur in clinical areas. CLINICALTRIAL
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