Do You Know the Medication Error Rate for Your Organization?
October 11, 2016 | Sean O'Neill
The answer to that question is NO. Some hospital leaders may take offense to this statement, but it is not an indictment of the quality of a particular organization’s safety program. Instead, it is a reflection of a painful truth about healthcare. While many organizations diligently track medication error reports, this data provides neither a valid nor reliable outcome measure to gauge the frequency and severity of patient harm from improper medication use.
By definition, an outcome measure determines how a system is ultimately performing. In an ideal world, every institution would have an outcome measure for medication safety, which would enable clinical leaders to clearly and accurately monitor safety, identify deficiencies and guide interventions. Such a measure would more judiciously allow organizations to track progress with the goal of eliminating patient harm from medication safety events.
Most hospital systems can provide a rate of “reported” medication safety events through voluntary reporting systems. However, this is a limited metric. Reporting can be impacted by multiple factors that determine whether or not an event is ever reported. These factors include, but are not limited to, the recognition that the error occurred, the reliability and availability of a reporting system, and—most importantly—the existence of a non-punitive safety culture that doesn’t dissuade clinicians from reporting safety events. Moreover, the variability of these factors among hospitals render benchmarking nearly impossible.
It is important to highlight that these limitations do not reduce the importance of tracking medication error reports. They provide an important way of identifying trends and serve as a foundation for further investigation of potential opportunities for harm. It does mean, however, that these reports cannot be used as a sound basis for determining the prevalence and severity of medication errors in a hospital or health system.
So, as medication safety leaders, how do we navigate the absence of a reliable outcome measure? One solution includes the use of process metrics to help measure the performance of high-risk processes that lead to medication errors. By monitoring these process metrics clinical leaders are able to assess how well (or how poorly) a system is working. Looking at data that strongly correlates with patient safety can serve as a useful proxy for that elusive outcome measure. Examples of high risk processes in the medication use continuum whereby such measurement is especially valuable include:
Medication reconciliation at transitions of care
Automated dispensing cabinet (ADC) overrides
Barcoding during medication administration
Smart infusion pump drug library utilization rates
Despite the promise of process metrics to better gauge outcomes, the data that informs these metrics are difficult to manage, and health systems struggle to capture and utilize this data efficiently and effectively. This happens for a number of reasons. First, the sheer volume of data can be overwhelming for clinicians who are not trained in analytics and do not possess the data science skills (from pivot tables to python) to be able to efficiently translate raw transactional data into clinically meaningful information. Second, the data comes from a variety of disparate sources and often requires labor-intensive aggregation, transformation and cleaning before any actual analysis can be started. Finally, clinicians simply do not have the time that is required to manage this process. Unfortunately, a job that ideally requires multiple FTEs most often only has a fraction of an FTE currently available.
As medication errors remain near the top of the list of drivers of harm in hospitals, the lack of a reliable outcome measure for medication safety events continues to be a challenge. The use of process metrics—although not the gold standard—is a way that we can bridge the gap in the pursuit of being able to identify vulnerabilities and measure the efficacy of the strategies we put in place to mitigate them. In order to do that successfully, we have to take data mining and data inputting off of the clinician’s plate. Instead, this data needs to be provided to our clinicians in a more efficient, actionable and clinically-relevant manner, ultimately allowing them to work at the top of their licenses.
See published post here.
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