. "6 Action Agenda for the Pharmaceutical, Medical Device, and Health Information Technology Industries ." Preventing Medication Errors: Quality Chasm Series. Washington, DC: The National Academies Press, 2007.
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Preventing Medication Errors
experienced with CPOE and pharmacy database systems. Alerts with infusion systems can be of particular concern since the highest percentage of medication errors is associated with intravenous medicines (Cohen, 2000; Billman, 2004). Smart-pump alerts may occur during programming or administration regardless of whether infusion rate limits are available for the drug (Malashock et al., 2004). The most common override occurs when the infusion rate is either above or below the maximum/minimum rate limit (Malashock et al., 2004). Other alerts can include informing the programmer of a duration change, a secondary stop, cancellation of drug selection, weight change–related dose recalculation, and same-drug infusion on multiple channels. Alerts can also occur for low battery power, venous occlusion, and similar conditions. The development of methods to rank the alert functions of infusion pumps could improve their functioning and safety.
While studies have clearly indicated that a tiered severity structure is important, additional work on how to differentiate, select, and integrate a separate tier for certain moderate-level alerts is required. A moderate-level warning may not be clinically important to one patient but may be for another, or may be important to a patient’s quality of life and adherence to medication therapy (Ahern and Kerr, 2003). A method for ranking the most frequent types of ADEs for each severity level also should be incorporated into the alert structure (Kilbridge et al., 2001; Miller et al., 2005). There have been many evidence-based studies identifying the frequency and severity of ADEs that can be used to determine these parameters (Classen et al., 1997; Forester et al., 2003, 2004; Gurwitz et al., 2005). Miller and colleagues (2005) suggest that the frequency rating might be based on percentage of total administrations, or 100 and 1,000 administrations. Table 6-2 provides a sample of the most common occurrences of alerts and the reasons for overriding them.
Standardization, however, does not imply rigidity. The alert configuration must remain flexible enough to reflect the inherent variability in clinical practice, such as the off-label use of a drug. In addition to severity and frequency, clinical importance is a third essential element of an alert structure. Alert ranking should be flexible enough to target the needs of specific patient populations (e.g., pediatric, geriatric) and medical disciplines (e.g., oncology, psychiatry) (Fortescue et al., 2003; Grasso et al., 2003). Most drug-related technologies allow for alert configurations according to patient age and weight, but are not designed to incorporate other individualized patient information. Hospital pharmacy database systems are the exception where laboratory test values can be linked to the patient’s medication management profile (IOM, 2004). As in the ambulatory care setting, alert rankings should reflect considerations specific to a patient’s condition or provider’s medical discipline (e.g., the alert ranking related to drug toxicity may be different for oncology than for nephrology). Such considerations do not