(Evans et al., 1994). Table 5-2 lists the most common sources of detected ADEs.

On the basis of its ADE causal analysis, the improvement team began to devise, test, and implement changes to drug ordering, delivery, and review systems within the hospital (rapid-cycle testing) (Classen et al., 1992; Evans et al., 1994). Figure 5-3 shows ADE rates at the hospital as the detection system was enhanced (1988–1990) and then as system fixes were implemented to prevent or reduce the consequences of ADEs (1993–1999). Several changes produced better performance. Under the data-based clinical trigger system, rapid case review by a pharmacist led to more rapid recognition of an event with immediate clinical reaction, averting some ADEs in earlier, less severe stages. The hospital’s electronic pharmacy system was programmed to recommend safer alternatives when a physician ordered highly allergenic medications. The electronic pharmacy system was also programmed to calculate ideal medication doses for each dose delivered, based on patient age, gender, and body mass; estimates of kidney function; estimates of liver function; and other blood chemistry values. It was demonstrated that similar results can be obtained without an electronic medication decision support system by having a pharmacist join physicians and nurses as they conduct patient rounds each day or by having pharmacists conduct their own independent patient rounds (Leape et al., 1999).

The same ADE prevention system was later deployed to sister hospitals in the region (deployment and implementation). The hospital system continued to monitor ADE rates to ensure that its investment in safer patient care did not deteriorate as organizational attention was shifted to other major sources of injury (holding the gains).

In March 2000, a visiting clinical researcher analyzed almost 10 years of data on ADEs detected by the hospital’s data-based clinical trigger system (Henz, 2000). As Table 5-2 shows, among more than 70 clinical triggers in active use during the trial, 14 accounted for more than 95 percent of all ADEs detected. A number of groups have used the resulting list of high-yield clinical triggers to build manual and automated ADE detection systems, with the aim of delivering safer care. More recent internal investigation has suggested that the data-based clinical triggers could be improved even further through examination of interactions among triggers on the list (Kim, 2003).

Other researchers have investigated enhanced case finding based on ICD-9 CM discharge abstract codes and E-Codes, followed by retrospective chart review using explicit criteria to detect ADEs. Initial results suggest that such methods can roughly double the total number of ADEs detected relative to those found by the data-based clinical trigger system (Xu et al., 2003). Such activities represent the start of a second major improvement cycle, which if successful, could lead to a further decline in the single largest source of care-related injuries Americans face when hospitalized.

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