We were able to successfully apply a laboratory-value trigger tool in an ambulatory care clinic and to achieve clinician agreement in ADE ascertainment using the chart review. We found a significant number of ADEs using a trigger tool. While the utility varied among the six laboratory value triggers, INR>5 efficiently identified ADEs.
Our determinations of preventability are consistent with prior literature showing that many ADEs are preventable.7
In this sample, only 3% of the triggers were felt to be non-preventable. In terms of clinical severity, the majority of ADEs identified by this trigger tool were in the mild-to-moderate-harm range. Consequently, there is an opportunity to intervene and prevent ADEs in the outpatient setting before significant harm occurs. We believe this underscores the importance of including ADE surveillance in outpatient safety initiatives.
Importantly, we found that most ADEs occurred during the self-management and monitoring stages of medication use, rather than being prescribing or dispensing errors. Therefore, interventions to prevent outpatient ADEs will need to be patient-centred and focus on safe medication self-management. Gaps in medication monitoring clearly led to ADEs, but there is scant evidence underlying current monitoring recommendations, and more study of ambulatory medication monitoring is needed.24
Because the number of medications is strongly associated with ADEs,10
with population ageing and increasing medication use we expect outpatient ADEs to become more frequent.
There were limitations to our study. We relied upon medical chart review to determine whether an ADE occurred. Similarly, because we relied on medical records, we could not pinpoint the cause of the ADE beyond the medication use stage. As an example, it is often unclear from chart documentation whether the error occurred because of a lack of monitoring or in the course of adhering to recommended medication monitoring. However, the use of a comprehensive electronic medical record mitigated some of the flaws and biases often associated with chart review studies. The patient population is from a single, ethnically diverse, safety net clinic setting and that is both a strength and weakness. It is a strength in that findings from this clinic are likely to have public health importance, but the population may be more ill and less comparable with the usual primary care population in USA.
While the INR >5 successfully identified ADEs, the other laboratory triggers had much lower yield than previously described.18
Several factors could contribute to this difference. First, our population has a significant prevalence of chronic kidney disease, as reflected in the above-threshold values for BUN and creatinine not attributable to ADEs. Similarly, there is a high prevalence of hepatitis C infection in this population that likely reduces the yield for ALT and AST trigger tools. Second, it is possible that the electronic medical record captures different information that the paper charts from the prior study. Third, because our patients are largely uninsured, virtually all of their laboratory tests and subspecialty care occurred within the integrated public healthcare delivery system. This may account for differences compared with the prior study.
Clearly, the performance of a trigger tool depends on its intended use. For our purposes of identifying patients with incipient ADEs in order to intervene clinically, we concluded that, with the exception of INR>5, using abnormal laboratory values is a ‘noisy’ method to identify the signal of ADEs. INR>5 did detect ADEs efficiently, and surveillance at this threshold may reduce the harm from ADEs associated with anticoagulation. For the other laboratory-based triggers, the labour associated with reviewing many charts in order to identify a single ADE was prohibitive for our clinical practice. For purposes of conducting research or for acquiring prevalence data for operational purposes, this method certainly would be more efficient than comprehensive or randomly selected chart review. Moreover, during the study period, there were 19 066 potentially eligible visits, so using the triggers did significantly reduce the pool of eligible records.
We were encouraged that the method of physician chart review resulted in reliable identification of ADEs, and our findings of problems in self-management and monitoring suggest the need for patient-directed interventions to reduce ADEs. Our results also suggest that in order to use trigger tools effectively, outpatient care settings may need to change the value at which a laboratory value triggers (the ‘trigger threshold’), considering other laboratory-based triggers, and, where feasible, consider testing text triggers. Finally, our findings imply that other tools, such as text triggers, or more complex automated screening rules which combine data hierarchically may be needed to efficiently screen for ADEs in adults seen in primary care.