Poon and colleagues set out to evaluate whether implementation of bar code technology reduced dispensing errors and the ADEs that might be caused by these miscalculations. In a before-and-after evaluation, they studied more than 350,000 dispensed medication doses in an academic medical center between February 2003 and September 2004.
During the bar code conversion process, the hospital pharmacy built a dedicated repackaging center, which was responsible for affixing a bar code to every dose of medication. These medications were then dispensed in three different configurations: two configurations required staff to verify all doses at least once using bar code scanning, and the third configuration—for commonly dispensed medications that could not be accommodated in a standard carousel machine because of their size or need for refrigeration—required scanning only one dose from each batch.
The authors found a 93% to 96% relative reduction in the incidence of target dispensing errors (P<0.001) and an 86% to 97% relative reduction in the incidence of potential ADEs (P<0.001) in the two configurations that required staff to verify all doses by scanning. The greatest reductions were seen in wrong medication errors (56%), wrong strength/dose errors (71%), wrong formulation errors (90%), and expired medication errors (100%).
In the configuration that did not require scanning of every dose, however, there was a 60% relative reduction in the incidence of target dispensing errors (P<0.001), but a 2.4-fold increase in the incidence of target potential ADEs. This included new errors attributable to wrong strength and wrong medication dispensing.
In light of the FDA’s mandate regarding bar codes, it seems that every hospital has the opportunity to improve patient safety and decrease medication error rates with the use of bar code technology. This study suggests that in order to achieve this benefit these systems should be designed to ensure that every medication dose is verified by scanning during the dispensing process.
Evaluation of a Guideline to Guide Resuscitation
By Cindy Lien, MD
Morrison LJ, Visentin LM, Kiss A, et al. Validation of a rule for termination of resuscitation in out-of-hospital cardiac arrest. N Engl J Med. 2006 Aug 3;355(5):478-487.
The survival rate of patients with out-of-hospital cardiac arrest is very low. Thus, guidelines have been developed for termination of resuscitation for those patients who have had no response to advanced cardiac life support provided by emergency medical service (EMS) personnel. Similar guidelines have not yet been developed, however, for situations in which patients receive basic life support from emergency workers trained in the use of an automated external cardiac defibrillator. Patients with little potential for survival are routinely transported to emergency departments, at significant cost to the healthcare system.
Morrison and colleagues present results from the Termination of Resuscitation (TOR) study, a prospective evaluation of a clinical prediction rule for the termination of basic life support by emergency medical personnel trained in the use of automated external defibrillators. The clinical prediction rule, previously developed in a retrospective review of case records from a large urban EMS system, recommends termination of resuscitation if there is no return of spontaneous circulation, no shock administered, and no witness of the arrest by EMS personnel.
In the current study, the authors obtained follow-up data for 1,240 adult patients in Ontario, Canada, who had suffered an arrest of presumed cardiac cause and were subsequently transported to the emergency department after resuscitative efforts. Twenty-four EMS systems participated in the study. The study found that only 0.5% of the patients for whom the clinical prediction rule recommended termination survived (four out of 776 patients). Of the 1,240 total study patients, 41 (3%) survived. The clinical prediction rule recommended continuation of resuscitative efforts for 37 of these 41 patients, resulting in a specificity of 90.2%. The positive predictive value for death was calculated to be 99.5% when termination was recommended.